The research distinguishes carefully between individual prejudice and structural racism, drawing on work by psychologists and sociologists who have mapped how discrimination becomes embedded in institutions. Individual prejudice typically occurs face to face, through microaggressions, hate speech or overt hostility. Structural racism, by contrast, is a web of interconnected elements embedded in legislation, policies and institutional practices that perpetually disadvantage racial minorities, even when individuals working within those systems hold no personal racist beliefs. The study cites evidence that structural racism manifests notoriously in housing, employment and the justice system, and it argues that supremacist attitudes also hide behind distancing strategies such as claims of color-blindness, whereby many white people detach themselves from responsibility and avoid discussing racism altogether. Philosopher Charles Mills describes this as a self-shielding tactic, in which pretending not to know functions as a channel that dismisses the oppressive realities of white supremacism.

Recent survey data underscore the scale of the gap between perception and reality. A 2025 Pew Research Centre survey of more than 5,000 respondents in the United States found that optimism about racial reform, which stood at 52 percent of US adults after the murder of George Floyd in 2020, had dwindled to 27 percent by 2025. Among Black respondents, 67 percent believed that meaningful reform remained a pipe dream. Ironically, 58 percent of white Americans believed racial equality had been restored. The study also points to a Gallup finding that 64 percent of US citizens consider racism against Black people to be widespread, and to research by the German Centre for Integration and Migration, which found in 2022 that 90 percent of respondents in Germany believed racism existed there. United Nations expert reports on the United Kingdom have concluded that discrimination against people of African descent is structural, institutional and systemic, while the European Union Agency for Fundamental Rights has reported that four out of ten Black people in Europe are stopped by police allegedly because of their skin color.

Central to the study is a genealogy of how racial ideology has adapted across economic eras, from mercantilism to industrial capitalism, imperialism and contemporary neoliberal globalization. Race, the author emphasizes, has no biological cogency: humans share roughly 99 percent of their DNA, and modern scholarship treats racial categories as political inventions created to grant power to white people and legitimize the subjugation of people of color. Racial hierarchies emerged historically around the fifteenth century and gained momentum alongside the transatlantic slave trade, colonial expansion and capitalist accumulation. The study documents how religion, culture, pseudo-science and economics were each mobilized to justify racial classification, from cherry-picked Christian doctrines that framed Black people as a cursed race, to Rudyard Kipling’s ‘The White Man’s Burden,’ to distortions of Darwinian evolution that recast imperial domination as ‘survival of the fittest.’ Legal instruments such as the Jim Crow laws and South Africa’s apartheid pass laws then entrenched segregation long after slavery’s formal abolition.

One of the study’s most detailed case studies concerns education and academic publishing, arenas the author describes as supposed champions of open inquiry that have themselves been captured by supremacist gatekeeping. Article 26 of the Universal Declaration proclaims that education shall strengthen respect for human rights and promote tolerance and friendship among racial groups, yet the study argues that Western curricula continue to elevate whiteness at the expense of other races, teaching slavery and colonialism in sanitized versions while concealing colonial atrocities such as German genocide in Namibia and British torture of Kenyan freedom fighters. In publishing, the author describes subtle gatekeeping coded in routine rejection reasons, including demands to tone down criticism of imperialism, reduce word counts that would otherwise cut off criticism, or conform to Western-prescribed methodology. Empirical studies corroborate the pattern: research on geoscience found that only about 30 percent of papers on African topics were written by African authors, while studies during the COVID-19 pandemic and in global oncology documented persistent under-representation of sub-Saharan African authorship.

The study links these knowledge hierarchies to global capitalism, arguing that supremacism sustains a Western hegemonic system in which might is treated as right. Through trade ploys described as colonial-based commodity racism, including control of plants and seeds, and through raced markets, capitalism is said to manufacture poverty in the Global South while marginalizing it in economic relations. The author invokes Achille Mbembe’s concept of necropolitics, in which radical disparities in wealth distribution are purposely reproduced as small doses of death in the daily lives of subaltern populations. Trade frameworks such as the African Growth and Opportunity Act are criticized for tying market access to US-defined benchmarks of liberal governance, and the funding dependence of African institutions, with the African Union receiving nearly 70 percent of its operational funding from Europe, is presented as a mechanism of quiet capture that erodes institutional independence.

International human rights law offers little shelter from these dynamics, the study finds, because laws enacted in good faith are subverted by institutional practice. The United Kingdom’s Equality Act 2010 and the Treaty of Lisbon, which made the Charter of Fundamental Rights of the European Union legally binding, are cited as milestone legislation that nonetheless coexists with pervasive, well-documented racism. The now-defunct Rwanda Asylum Plan, struck down by four separate courts as a breach of the UK’s international treaty obligations, is presented as an example of how governments persist with policies widely denounced as racist even after legal defeat. The paradox, the author argues, is devastating for the credibility of universal rights: it seems implausible that universal rights can be attained in environments that condone supremacism, prejudice and racial stereotyping, and many in the Global South consequently view the human rights agenda as an extension of disguised colonialism.

The study also engages critically with psychological theories of racism’s origins, including Frances Welsing’s Cress theory of color-confrontation, which posits that white supremacy is driven by a fear of genetic annihilation in a world where most people are non-white. The author reviews objections from scholars such as Bernard De Montellano, who found the albinism claim scientifically baseless, and from critics who argue the theory distracts from the political economy of racism, exemplified by historian Barbara Fields’s warning that slavery’s chief business was the production of cotton, sugar, rice and tobacco rather than white supremacy. The study concludes that the real drivers of supremacism are historical capitalist economic factors, profit accumulation through cheap labor and the fear of losing unearned imperial and neocolonial privileges, rather than genetics.

Resistance, the study insists, must come from both sides of the racial divide, because complicity sustains the system. Philosopher Iris Marion Young’s social connection model holds that people are responsible for structural discrimination by virtue of participating in the institutions they condemn, through employment, volunteering or funding. The author cites Steve Biko’s call for Black people to reclaim agency and Nelson Mandela’s reminder that no one is born hating another person because of the color of their skin, since racism is a taught behavior. At the same time, the study argues that white complicity persists through color-blindness, white fragility and tokenism, the perfunctory hiring of minority members to create a false impression of inclusiveness while denying them real influence over policy. Emerging counter-currents, including South-South cooperation through BRICS and the decolonial drive toward delinking from the colonial matrix, are flagged as signs that the cycle of coloniality may be nearing its end.

The study’s conclusion is blunt: universal human rights and global justice cannot be attained when certain groups believe themselves above the structures of international governance, and the greatest threat to international peace and security may not be armed conflict but white supremacism itself, which fuels conflict through capitalism’s profit-maximizing contempt for African lives. Unless there is a proactive policy shift, the author warns, polarization between the West and Africa will deepen, making sustainable global security harder to achieve. Invoking an African proverb that when you shoot a zebra in the black stripe, the white stripe dies too, the study frames racism as a self-defeating scheme in a world where all human beings are enmeshed in the same social fabric, sharing 99 percent of their DNA and, by the terms of every major human rights covenant, the same equal and inalienable rights.

Subject of Research: How white supremacism undermines the universal human rights agenda through structural racism and global capitalism

Article Title: How white supremacy undermines the universal human rights agenda

Article References: Simuziya, N. J. (2026). How white supremacy undermines the universal human rights agenda. Discover Global Society, 4(1), Article 233. https://doi.org/10.1007/s44282-026-00544-x

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00544-x

Keywords: white supremacy, human rights, UN Charter, structural racism, capitalism, Global South, colonialism, academic publishing, racial discrimination, institutional racism, necropolitics, decoloniality

The study, authored by Brady Kennedy of Columbia University’s Department of Sociology, Cristian Capotescu of Columbia’s Trust Collaboratory, and Jennifer Laird of Lehman College, challenges the prevailing assumption that trust in smart city technologies can be engineered through better privacy protections, more reliable sensors and clearer communication. Drawing on a thematic synthesis of sociological research on trust alongside the literature on public participation, the authors contend that trust is not an attitude stored in the minds of individuals and measured by surveys, but a property of ongoing social relations between residents and the experts and institutions behind these initiatives.

Smart city initiatives typically combine several layers of technology: distributed sensors and connected devices grouped under the heading of the Internet of Things, which collect data from the built environment; information and communication technologies and cloud computing, which transmit and process that data at scale; and increasingly, artificial intelligence and algorithmic decision-making, which translate the resulting data into classifications and predictions for operational use. Stated aims range from optimizing public services and improving environmental sustainability to making infrastructure more accessible and cities safer and more livable.

Much existing research on trust in smart cities operates within a risk-benefit logic. Scholars have produced testable models simulating how mistrust hinders deployment, measured public attitudes through surveys, and proposed lists of trust characteristics such as privacy, reliability and fairness that designers can integrate into systems. The assumption is that as initiatives follow prescribed security parameters, privacy standards and governance norms, and communicate those choices transparently, public trust will follow. Kennedy and colleagues call this a technology-centric view that treats trust as flowing in one direction, with the public asked to extend it according to how experts adjust technical features.

In contrast, recent sociological scholarship, particularly from the field of science, technology and society, understands trust as inherently relational. In this account, trust emerges from interactions between trustors, the people doing the trusting; trustees, those in whom trust is placed; and trust objects, the specific focus of the relation at a given time and place. A smart city initiative may be the object of a trust relation, but it is never the trustee: residents extend or withhold trust with respect to the agency, vendor or partnership that designs and operates the system. Crucially, because uncertainty is what makes trust necessary in the first place, its conditions can never be fully engineered away through technical decisions.

The authors further draw on work describing trust as a verb, an active practice of trusting. Trusting is a skillful evaluative process in which residents scrutinize information and conduct rather than placing blind faith in experts. This carries a demand for both sides: the trustor must do the work of assessment, and the trustee must render itself answerable to that scrutiny. Because the trustee in these relations is an institution whose authority is itself at issue, answerability requires that decisions, and the power behind them, be made contestable. Notably, the authors argue that mistrust is not a failure of trust-building but internal to the same relational ties, playing an important role in promoting accountability and public engagement.

From this foundation, the study develops a typology of three participatory roles: the public as co-accessors, co-collectors and co-designers. Each role names the position residents occupy relative to smart city experts, and, because a relation cannot be specified from one side alone, also names what experts must place at stake. As co-accessors, residents shape, monitor and contest how data is gathered and used, while institutions accept the vulnerability of scrutiny. Examples range from static open data dashboards such as London’s DataStore to deeper arrangements like Barcelona’s New Data Deal, which re-envisions collected data as a public commons, and Manchester’s data governance practices, where residents and policymakers negotiate data gathering within structured engagements.

As co-collectors, residents directly produce data for smart city systems, through passive smartphone sensing or active crowdsourcing, while experts accept dependence on evidence they did not themselves produce. The MiraMap platform in Torino, Italy, allows residents to report and track urban problems, research showing it increased trust in government and technology alike. At the furthest extent, residents deploy their own sensors through systems such as HabitatMap AirSense and AirBox to generate environmental evidence independent of official accounts, converting mistrust into an instrument of accountability. As co-designers, finally, residents participate in shaping what is built, with experts relinquishing exclusive control. Iterative workshop approaches that visibly incorporate resident feedback have been shown in follow-up surveys to increase community trust in both the technology and the researchers, while game-based and virtual reality formats help include elderly and non-technical participants.

The framework carries pointed lessons for practitioners. Tokenism and consultation that leave residents feeling passively managed are not failures of technique but interactions in which no reciprocity was established; the mistrust that follows is a proportionate response, as illustrated by the urban data trust proposed for Sidewalk Toronto, constituted by the operator on the operator’s own terms, which failed to generate the mutuality on which trusting depends. The authors also caution that participatory approaches do not encounter a ready-made public but bring one into being, and that formats demanding time, mobility and technical confidence tend to reproduce existing inequalities, underrepresenting marginalized groups. Compensation and trusted community intermediaries can help widen who participates.

Ultimately, the authors argue, smart city professionals should stop treating participation as an instrument for raising approval scores and start treating it as the intentional structuring of reciprocal interaction in which trust, and mistrust, can both operate productively. There is no single modality for building trust, and each initiative will pose context-specific challenges. But without a relational understanding in which experts make themselves genuinely answerable, they warn, even well-intentioned future initiatives will face the same public backlash that has already derailed smart city projects from Toronto to San Diego.

Subject of Research: Trust-building through public participation in smart city initiatives

Article Title: Reframing participatory approaches to the smart city as opportunities for trust-building

Article References: Kennedy, B., Capotescu, C., & Laird, J. (2026). Reframing participatory approaches to the smart city as opportunities for trust-building. Discover Cities, 3(1), Article 177. https://doi.org/10.1007/s44327-026-00352-7

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00352-7

Keywords: smart cities, public trust, public participation, sociology of trust, STS, expertise, data governance, participatory design, citizen sensing, urban technology, co-design, mistrust

The research team, led by Maria Dolores Vivas-Urias, Cristina Obispo-Diaz, and Maria Auxiliadora Ruiz-Rosillo, tracked 180 university teachers across two consecutive academic years, 2023–2024 and 2024–2025, using a pre-experimental one-group pre-test and post-test design. Rather than comparing trained teachers against an untrained control group, the study measured the same cohort twice: once before and once after the rollout of the university’s GenAI Institutional Plan 2023–2025. This design allowed the researchers to capture within-person change on three core dimensions: general awareness of generative AI, beliefs about its impact on professions, and the perceived importance of learning AI tools for integration into student training.

The headline result is striking in its magnitude. Teachers’ self-reported awareness of generative AI rose by an average of 2.24 points on a ten-point scale, climbing from 4.82 to 7.06, a difference the authors characterize as clear and statistically significant. Perceptions followed a parallel trajectory. By the second year, 80.9 percent of surveyed teachers believed generative AI would transform the way they teach, an increase of 3.76 percentage points. Belief in the technology’s impact on students’ professional futures also rose, with the overall mean score increasing from 7.97 to 8.67 out of 10. Perhaps most tellingly for institutional planners, the perceived importance of learning AI tools for integration into student training reached 8.63 in 2024–2025, a statistically significant gain of 0.56 points over the previous year.

Beneath these averages lies a structural insight that may prove the study’s most consequential contribution: awareness, perceived professional impact, and the motivation to learn form an interlocking psychological chain. Correlation analysis revealed a strong positive relationship of r = 0.718 between teachers’ perceptions of generative AI’s impact on students’ professional futures and the importance they attached to learning AI tools. A more moderate correlation of r = 0.316 linked overall AI awareness to perceived professional impact. In practical terms, teachers who understand the technology better are more convinced it will reshape their fields, and that conviction drives their appetite for training. The authors interpret this through established frameworks such as the Technology Acceptance Model, in which performance expectations, the belief that adopting a technology improves outcomes, fuel adoption.

The institutional machinery behind these shifts deserves scrutiny. UAX launched its GenAI Institutional Plan in the 2023–2024 academic year, establishing milestones for both short- and medium-term transformation. The centerpiece for faculty was a compulsory training program, the Level II Teaching Certification in Generative Artificial Intelligence, deliberately aligned with UNESCO’s AI Competency Framework for Teachers across its five dimensions: a human-centred mindset, ethics of AI, AI foundations and applications, AI for professional learning, and AI pedagogy. The certification combined asynchronous online micro-courses introducing fundamentals, ethical use, data privacy, and academic integrity with face-to-face or synchronous workshops in which teachers practiced prompt design, generated learning activities mapped to Bloom’s Taxonomy, and built assessment strategies and personalized educational materials.

Participation figures point to genuine appetite rather than mere compliance. Of 1,300 invited teachers, 65 percent engaged in at least one training activity, and 428 completed the full certification. Satisfaction ratings were high across the board: the certification scored 8.6 for course level and 8.5 for usefulness for professional development, while the shorter two-hour practical workshops scored even higher, reaching 9.1 and 9.2 on usefulness and recommendation respectively. Notably, 72 percent of participants reported they had been able to apply what they learned in their personal and professional lives, and the training’s culminating activity required each teacher to submit a concrete proposal for integrating AI into their own teaching practice. That contrasts with prior research in which educators reported strong general technology training yet still felt unprepared to work generative AI into their classrooms.

Behavioral indicators tracked alongside perceptions tell a consistent story. The share of teachers reporting no use of AI tools collapsed from 54.38 percent in 2023–2024 to 10.42 percent a year later, and use of AI for teaching purposes jumped from 16.59 percent to 43.40 percent of responses. ChatGPT remained the dominant tool, cited by 89.14 percent of users in 2024–2025, but Copilot usage rose sharply, from 7.57 percent of responses to 28.99 percent, likely aided by the institution’s provision of Microsoft Copilot through university Office 365 accounts. Nearly half of the sample, 45.73 percent, used both tools concurrently. Because 72.6 percent of participating teachers held part-time contracts and worked outside the university, the faster uptake in teaching contexts relative to professional practice suggests that institutional training, rather than external professional exposure, was the decisive accelerant.

The study is candid about its constraints. The absence of a control group means the changes cannot be causally attributed to the training program alone; broader cultural momentum around generative AI between 2023 and 2025 undoubtedly contributed. The voluntary response rate was low, at 16.6 percent of the overlapping faculty population, raising the possibility of self-selection bias in which the most motivated teachers dominated the sample. The measurement instrument itself, a three-item one-dimensional perception scale, showed acceptable reliability with a Cronbach’s alpha of 0.72 and omega of 0.76, but the authors acknowledge that the saturated factor model limits interpretive weight of the confirmatory fit indices, and the modest Kaiser–Meyer–Olkin value of 0.59 indicates restricted factorability that future work should address. Reliance solely on teacher self-report, without observational evidence of changed classroom practice, further narrows what the data can establish.

Qualitative responses flesh out where teachers want to go next. An inductive thematic analysis of open-ended answers from 67 respondents identified seven broad categories and 29 specific training needs, clustered around pedagogical integration of generative AI, personalized content creation, automated assessment, ethics and responsible use, contextualized technical development, and certified continuing education. The authors note that most of these demands map onto the existing certification program, but more technical requests, such as discipline-specific tool development, will require specialized professional development beyond the current generalist offer. Faculties of Education Sciences and Technology and Business led awareness scores in both years, while significant pre-post gains appeared across Healthcare Sciences, Medicine, Dentistry, and Veterinary Medicine, indicating that the training effect penetrated fields far removed from computer science.

For the global higher education sector, the study offers a rare data point in a debate dominated by policy documents rather than measured outcomes. Recent international analyses have catalogued how universities are issuing guidelines, redesigning assessment, and drafting ethical codes, but few have quantified how such institutional efforts move the people on the front lines. The UAX results support a systemic, ethical, and sustainable strategy in which AI literacy is treated as an ongoing faculty competency rather than a one-off compliance exercise, and in which generative AI is embedded directly into curricula to close the gap between university education and labor market demands. The authors recommend that future research measure the transfer of training into actual teaching practice, incorporate student perspectives, and complement self-report with behavioral evidence. If replicated with stronger designs, the core message, that well-structured institutional training can convert teacher anxiety about AI into informed professional momentum, could reshape how universities worldwide approach the fastest-moving educational transformation of the decade.

Subject of Research: University teachers' perceptions of generative artificial intelligence following an institutional training plan in higher education

Article Title: Analysis of teachers’ perceptions of the impact of Generative Artificial Intelligence in higher education

Article References: Vivas-Urias, M. D., Obispo-Díaz, C., & Ruiz-Rosillo, M. A. (2026). Analysis of teachers’ perceptions of the impact of Generative Artificial Intelligence in higher education. Journal of New Approaches in Educational Research, 15(1), Article 12. https://doi.org/10.1007/s44322-026-00060-5

Image Credits: AI Generated

DOI: 10.1007/s44322-026-00060-5

Keywords: generative artificial intelligence, higher education, teacher training, AI literacy, faculty perceptions, institutional policy, UNESCO AI competency framework, ChatGPT, educational innovation, pre-test post-test study, teacher professional development, Spain

Autism is a developmental condition that emerges in early childhood and persists across the lifespan, profoundly shaping social behavior and often making the acquisition of learning and social skills more difficult. According to data cited by the U.S. Centers for Disease Control and Prevention, autism spectrum disorder now affects a substantial and growing share of children worldwide, which has intensified the search for educational tools that can supplement traditional teaching. Over the past two decades, interactive technologies—from computer-based programs to tablet applications—have been explored as supports for autistic learners, and social robots in particular have attracted intense interest from researchers and clinicians alike.

The logic behind robot-assisted intervention rests on a striking observation: many children with autism engage more readily with machines than with people. Robots are predictable, their expressions are simplified, and their behavior is consistent and rule-governed. For children who find the rapid, ambiguous signals of human social interaction overwhelming, a robot can act as a social intermediary—demanding enough to practice attention and turn-taking, yet simple enough to feel safe. Numerous studies on autism intervention have highlighted the effectiveness of social robots in behavioral treatments, including work showing that the NAO robot can improve eye-gaze attention in children with high-functioning autism, and that long-term child-robot interaction can sustain attention and engagement over repeated sessions.

What has been missing, the researchers argue, is evidence from authentic classroom settings. Most robot-assisted autism studies have taken place in laboratories or clinics, in structured dyadic interactions between a single child and a single robot. But learning in schools is inherently social and collective: children must share a teacher’s attention, follow group instructions, and navigate the presence of peers. Reviews of field-based studies of social robots in classrooms confirm that genuine classroom integration remains sparse. The new study was designed to begin filling that gap by placing the NAO robot directly into a group teaching context at a special education school, with human teachers and the robot working side by side.

The experimental design was deliberately collaborative. Rather than replacing the teacher, the NAO robot functioned as a co-facilitator of classroom activities. Special education teachers led sessions in partnership with the robot, creating a triadic learning environment in which interactions flowed among teacher, robot, and students. This arrangement reflects principles from established autism education frameworks such as TEACCH, which emphasizes structured teaching, and applied behavior analysis, which underpins many technology-mediated interventions. By distributing instructional roles between a sensitive human professional and a predictable, engaging machine, the researchers sought to foster a dynamic learning environment that neither agent could create alone. The study, conducted at Changshu Special Education School, was explicitly framed as a foundational investigation—a proof of concept in anticipation of extended robot-assisted classroom sessions to be introduced at a later date.

The technical appeal of the NAO platform is central to the study. NAO is a 58-centimeter-tall humanoid robot developed by SoftBank Robotics, equipped with cameras, microphones, tactile sensors, and articulated limbs that allow it to gesture, dance, speak, and emulate human movement. Its child-sized stature and expressive but simplified face reduce the perceptual complexity that often challenges children with autism. Research on gaze perception has shown that decoding gaze direction from combined head and eye rotations is an integrative challenge that differs in autistic individuals; NAO’s exaggerated, unambiguous head turns and eye movements sidestep much of that ambiguity, making it an ideal cueing device for directing attention toward learning materials. Prior work has also demonstrated that robots can reduce delays in gesture production among preschoolers with autism, suggesting that the platform’s motor expressiveness carries direct pedagogical value.

The study’s data told a clear story. Students with ASD in classrooms equipped with the NAO robot exhibited notably improved performance compared to students in regular classrooms. The researchers’ preliminary findings indicate that the robot significantly enhanced focus and classroom engagement among students with autism—two behavioral pillars on which nearly all other classroom learning depends. Improved attention, in turn, appears to translate into better educational performance and potentially enhanced social functioning. These outcomes align with a broader literature: studies of robot-mediated group instruction and robot-assisted psychosocial interventions have reported gains in attention, imitation, and social responsiveness, while systematic reviews of robotics protocols for students with autism have called for exactly this kind of ecologically valid classroom evidence.

The implications extend beyond special education. Classroom bonding is widely recognized as a foundation for healthy development, and children with autism are at elevated risk of disengagement from school environments that feel socially punishing. If a social robot can lower the barrier to participation—making group instruction feel approachable rather than threatening—the technology could help close an achievement gap that has proven stubbornly resistant to conventional approaches. The findings also carry practical weight for teachers: the robot-assisted model tested here positions NAO as an assistant that amplifies, rather than supplants, professional expertise. The teachers retained authority over pacing, content, and behavior management, while the robot contributed attention capture, novelty, and motivational energy that human instructors alone often struggle to sustain across a full group of autistic learners.

Cautions remain. This was a foundational study conducted at a single special education school, and the authors themselves describe it as a first step toward extended robot-assisted classroom sessions. Questions about long-term novelty effects—whether children’s fascination with a robot fades after weeks or months—persist in the literature, even as some long-term engagement studies suggest sustained benefit. Sample sizes in robot-assisted autism research are typically modest, and generalizing from one school to diverse educational systems will require replication. Ethical safeguards, however, were carefully observed: the study received approval from the Institutional Review Board of Suzhou University of Technology, written consent was obtained from guardians and cognitively capable children, and video data were encrypted after encoding to protect privacy.

Even with those caveats, the study marks a meaningful shift in the trajectory of social robotics for autism. For years, the field has demonstrated that robots can capture the attention of autistic children in controlled settings; the harder question has always been whether that magic survives contact with real classrooms, real curricula, and real group dynamics. By showing that a teacher-and-robot partnership can measurably improve focus and performance among students with autism in an actual school, the researchers provide the most convincing answer yet that social robots belong not just in therapy rooms, but at the front of the class. As extended sessions begin, the world may be watching a small humanoid robot help rewrite what inclusive, effective education looks like for children on the autism spectrum.

Subject of Research: The use of the NAO social robot to improve classroom performance and engagement of students with autism spectrum disorder

Article Title: Classroom Performance of Students with Autism in Interaction with the NAO Robot

Article References: Feng, H., Yang, Q., Lu, H., & Gong, S. (2026). Classroom Performance of Students with Autism in Interaction with the NAO Robot. Frontiers of Digital Education, 3(2), Article 9. https://doi.org/10.1007/s44366-026-0083-1

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0083-1

Keywords: autism spectrum disorder, NAO robot, social robotics, robot-assisted classroom, special education, classroom engagement, human-robot interaction, educational technology, autism intervention, attention and focus, Classroom, Performance

The study, published in Discover Global Society, was grounded in Social Bond Theory, the classic criminological framework developed by Travis Hirschi in 1969. The theory holds that people refrain from deviant behaviour because of their ties to conventional society: attachment to significant others, commitment to conventional goals, involvement in legitimate activities, and belief in the moral validity of social rules. Religion, the researchers reasoned, should strengthen all four bonds. It attaches believers to a moral community whose disapproval they fear, commits them to a virtuous identity that would be costly to compromise, involves them in time-consuming prosocial activities, and bolsters belief in a transcendent moral code that condemns dishonesty. If the theory holds, more religious students should misbehave less.

To test this, the team measured religiosity along two dimensions: religious principles, capturing internalised beliefs and moral convictions, and religious practices, capturing observable expressions of faith such as participation in organised religious activities. Deviance was disaggregated into three distinct domains rather than treated as a single blob. Academic dishonesty covered examination malpractice and plagiarism; moral deviance captured ethical transgressions in which personal gain is prioritised over moral standards; and social deviance covered behaviours that undermine institutional and peer norms within the university environment. Respondents answered scenario-based questionnaire items describing realistic ethical dilemmas, rating on five-point scales how much they would engage in or justify the behaviour described.

The analytical machinery was rigorous. The researchers used partial least squares structural equation modelling, or PLS-SEM, a technique well suited to exploratory models with complex relationships and non-normal data. The measurement model passed standard checks: internal consistency, convergent validity and discriminant validity were confirmed using Cronbach’s alpha, composite reliability, average variance extracted, the Fornell-Larcker criterion and the heterotrait-monotrait ratio, with all HTMT values falling well below the conservative 0.90 threshold. Variance inflation factors ranged from 1.480 to 1.986, far beneath the critical value of 5, ruling out multicollinearity. Bootstrapping with 5,000 resamples tested the significance of path coefficients, and interaction terms probed whether sex, age, educational level and religious affiliation moderated the effects of religiosity.

The descriptive results painted a familiar picture of a devout student body. Overall religiosity scored a mean of 3.98 on a five-point scale, firmly in the high range, with religious practices scoring especially high at 4.42. Yet deviance sat at a moderate 3.12, with moral deviance slightly higher at 3.21. In other words, students maintained high levels of religious commitment while still reporting a notable susceptibility to misconduct. The sample itself was predominantly female, at 61.7 percent, overwhelmingly Christian at 87.2 percent, and mostly aged between 21 and 25, with final-year Level 400 students making up two-thirds of respondents.

The structural model delivered the study’s most provocative findings. Contrary to the protective expectation of Social Bond Theory, the significant direct relationships between religiosity and deviance were positive rather than negative. Religious principles showed a significant positive effect on moral deviance, with a path coefficient of 0.209, meaning students with stronger internalised religious beliefs reported greater susceptibility to morally deviant behaviour. Religious practices positively influenced social deviance with a coefficient of 0.180. Neither dimension of religiosity showed a significant direct relationship with academic dishonesty at all. The model explained 30.8 percent of the variance in academic dishonesty, a moderate figure, but only 21.6 percent for moral deviance and 15.3 percent for social deviance, with modest predictive relevance across the board.

How can stronger faith predict more deviance? The researchers offer several tentative explanations rather than definitive conclusions. One possibility is that religious belief may be internalised at a symbolic or identity level without translating into behavioural regulation, so that outward religiosity coexists with weak moral internalisation. Another is that individuals apply moral standards selectively, particularly when personal benefit is at stake. The finding echoes a broader paradox documented in prior research: in highly religious societies, strong public expressions of faith can coexist with elevated corruption, suggesting that religious identity may function as a source of social legitimacy or even moral cover that masks unethical conduct. Concepts such as moral licensing, in which being good frees people to be bad, have been proposed to explain such patterns, though the study did not directly test these mechanisms.

The moderation analysis added crucial nuance, revealing that the influence of religiosity is context-dependent. Sex moderated the link between religious practices and academic dishonesty, with simple slope analysis showing the relationship was strongest among female students and nearly flat among males. Educational level moderated the connection between religious principles and moral deviance, with the association growing stronger as students progressed from Level 300 to Level 400, possibly reflecting a rising tension between dogmatic religious frameworks and the critical thinking fostered by higher education, or increasingly sophisticated rationalisation among senior students. Religious affiliation moderated the effect of religious practices on academic dishonesty, with the relationship strongest among adherents of African Traditional Religion, moderate among Muslims, and weakest among Christians. Age, by contrast, moderated nothing, likely because the sample’s age distribution was too homogeneous to reveal meaningful variation.

The absence of any significant link between religiosity and academic dishonesty carries its own message. Academic dishonesty is often driven by situational pressures such as performance expectations, peer norms and perceived enforcement mechanisms, rather than by individual moral dispositions alone. This suggests that in highly structured academic environments, contextual and institutional factors may swamp whatever protective influence religious orientation might otherwise exert. The authors caution that the statistically significant findings should be interpreted carefully, given the number of effects tested and the small effect sizes observed, which raise the possibility of chance associations.

The practical implications are sobering for university administrators. Promoting religiosity alone, the study concludes, is unlikely to be sufficient as a strategy for strengthening ethical behaviour among students. Instead, institutions should adopt broader approaches to academic integrity that combine ethical education, robust enforcement mechanisms and supportive learning environments, approaches that prior research suggests are more effective than value-based interventions alone. The findings also carry theoretical weight, challenging oversimplified narratives that equate religious commitment with ethical infallibility and supporting more differentiated conceptualisations of religiosity that distinguish between belief systems and behavioural expressions.

The study has clear limitations. Its cross-sectional design precludes causal inference, self-reported measures of sensitive behaviours invite social desirability bias, and the sample, drawn from disciplines that explicitly engage with ethics and religious thought, limits generalisability to other fields. The researchers themselves note that Social Bond Theory was extended rather than directly tested, since religiosity was operationalised through only two constructs that partially map onto the theory’s four bond dimensions. Future work, they suggest, should incorporate longitudinal designs, behavioural tasks, peer reports, personality traits such as honesty-humility, and measures of moral disengagement, alongside qualitative research into how deeply religious students rationalise misconduct.

What emerges is a portrait of faith on campus that resists easy moralising. Religiosity does not exert a uniformly protective influence on student behaviour; it operates in selective and context-dependent ways across academic dishonesty, moral deviance and social deviance, shaped by sex, educational level and religious affiliation in ways that standard theory did not predict. For a country where faith saturates public life, and for universities worldwide grappling with cheating scandals and ethical erosion, the message is that visible piety and ethical conduct are not the same thing, and that safeguarding integrity requires institutional design, not just moral exhortation.

Subject of Research: The influence of religiosity on academic dishonesty and moral and social deviance among higher education students in Ghana

Article Title: Investigating the influence of religiosity on academic dishonesty and moral and social deviance among higher education students

Article References: Mensah, E., Owusu, M., Frimpong, E. A., Nyamekye, E., Ampem, I. O., Kwarteng, S., Dorm, E., & Kwofie, G. N. (2026). Investigating the influence of religiosity on academic dishonesty and moral and social deviance among higher education students. Discover Global Society, 4(1), Article 232. https://doi.org/10.1007/s44282-026-00601-5

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00601-5

Keywords: religiosity, academic dishonesty, moral deviance, social deviance, higher education, Social Bond Theory, PLS-SEM, Ghana, undergraduate students, academic integrity, moderating effects, ethical behaviour

The core premise is straightforward: science produces knowledge, but the way that knowledge circulates is governed by conventions that were established in an era of print journals, small laboratories, and slow communication. Those conventions persist even though the underlying infrastructure has been transformed. Preprint servers, open-access repositories, collaborative platforms, and data-sharing mandates have removed many of the technical barriers to rapid, transparent dissemination. Yet, as the commentary emphasizes, the cultural expectations surrounding credit, career advancement, and publication have not kept pace. Researchers still face powerful incentives to hoard data, to slice findings into the least publishable units, and to prioritize novelty over rigor, because those behaviors are what traditional evaluation systems reward.

Knowledge sharing sits at the center of the argument. The authors contend that openness should not be framed as an additional burden placed on already stretched researchers, but as a redesign of the default workflow. When data, code, protocols, and negative results are shared as a matter of routine, the entire enterprise becomes more efficient and more trustworthy. Other teams can verify findings, reuse materials, and avoid duplicating failed experiments. Systematic reviews and meta-analyses become more complete because the file drawer problem, in which unflattering results disappear from the literature, is mitigated at the source. The commentary suggests that institutions can accelerate this shift by embedding sharing requirements into grant conditions, laboratory onboarding, and graduate training, so that open practices become habitual rather than heroic.

Training emerges as a recurring theme throughout the piece. Cultural change in research, the authors argue, cannot be imposed from the top down alone; it must be cultivated in the next generation of scientists. Doctoral programs and early-career mentoring shape the professional identities of researchers more powerfully than any policy document. If mentorship continues to signal that impact factors and first-author papers in prestigious venues are the only currency that matters, then open-science mandates will remain symbolic. Conversely, when senior researchers model transparent practices, discuss failures openly, and reward careful replication work, junior scientists receive a coherent message about what constitutes good science. The commentary calls for structured programs that teach not only technical skills but also the norms of collaborative, reproducible research.

Evaluation reform receives equally sustained attention. The piece situates its argument within the broader international movement, exemplified by initiatives such as the San Francisco Declaration on Research Assessment and the Leiden Manifesto, that seeks to move institutions away from crude journal-level metrics as proxies for individual quality. The authors argue that evaluation systems function as the immune system of research culture: they determine which behaviors are accepted and which are rejected. As long as hiring, promotion, and funding decisions hinge on publication counts and journal prestige, researchers will rationally optimize for those signals, even at the expense of rigor, openness, and collegiality. Alternative approaches, including narrative curricula vitae, portfolio-based assessment, and explicit weighting of contributions such as peer review, mentoring, data curation, and community service, are presented as practical mechanisms for broadening the definition of scientific merit.

The commentary is careful to acknowledge that evaluation reform is difficult precisely because metrics are convenient. Quantitative indicators allow committees to compare large numbers of candidates quickly and appear objective. Replacing them with qualitative judgment requires time, training, and trust in evaluators. The authors respond that the apparent objectivity of citation counts conceals well-documented distortions, including field differences, self-reinforcing citation networks, and susceptibility to gaming. A more honest system, they suggest, would combine structured qualitative assessment with responsible use of quantitative evidence, applied at the level of individual contributions rather than journal brands. Several funding agencies and universities have already begun experimenting with such frameworks, and the commentary argues that these experiments should be evaluated with the same empirical rigor that scientists demand in their own research.

Community engagement forms the third pillar of the proposed cultural transformation. The authors argue that research culture is not confined to the walls of academia; it extends to how scientists relate to the public, to practitioners, and to the communities affected by their work. Participatory research models, citizen science, and co-design approaches are highlighted as strategies that both improve the relevance of research and redistribute epistemic authority. When patients, teachers, policymakers, or community members help formulate research questions, the resulting studies are more likely to address genuine needs, and the findings are more likely to be trusted and used. Engagement, in this framing, is not a dissemination afterthought but a constitutive part of the scientific process that begins at the stage of question selection.

The piece also addresses the structural conditions that make cultural change possible. Individual researchers, however motivated, operate within systems shaped by institutions, funders, publishers, and learned societies. The commentary therefore advocates coordinated action across these actors: funders can align grant criteria with open practices; institutions can reform promotion guidelines; publishers can support transparent peer review and registered reports; and societies can convene communities to develop shared norms. The authors emphasize that partial, fragmented reforms risk producing cynicism, since researchers asked to adopt new practices without corresponding changes in evaluation will experience openness as a cost with no return. Alignment across the system is presented as the decisive factor separating genuine transformation from superficial compliance.

Importantly, the commentary treats research culture as an empirically tractable subject. Rather than exhortation alone, the authors call for studying cultural interventions themselves: measuring whether particular mentoring programs, assessment reforms, or engagement strategies actually change behavior, improve reproducibility, or increase public trust. This meta-scientific stance reflects the broader movement toward the science of science, in which the research enterprise becomes an object of systematic investigation. By applying the same standards of evidence to cultural interventions that are applied to laboratory experiments, the field can learn which strategies work, under what conditions, and for whom, and can avoid investing in well-intentioned initiatives that fail in practice.

The appearance of this argument in npj Science of Learning is itself significant, since the journal sits at the intersection of education, psychology, and neuroscience, fields that have confronted reproducibility challenges directly and that depend heavily on public trust to justify their societal value. The commentary’s message is ultimately one of cautious optimism: research culture is not an immutable inheritance but a set of practices that scientists themselves created and can therefore remake. By aligning knowledge sharing, evaluation, and community engagement, the authors contend, the research enterprise can become more rigorous, more equitable, and more responsive to the societies that sustain it, provided that institutions, funders, and individual researchers act together rather than waiting for others to move first.

Subject of Research: Strategies for fostering cultural change in the research enterprise through knowledge sharing, research evaluation reform, and community engagement

Article Title: Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies

Article References: Rho, J., Sheu, J.-K., Forbes, A., Tsai, D. P., Alú, A., Li, W., Brongersma, M. L., Choi, J., Garcia de Abajo, F. J., Na Liu, L., Szameit, A., Schloemer, T., Tittl, A., Chemnitz, M., Wang, C., Zhang, J., Kivshar, Y., Cui, T. J., Ma, R.-M., … Matricardi, C. (2026). Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies. npj Science of Learning, 11(1), Article 49. https://doi.org/10.1038/s41539-026-00449-z

Image Credits: AI Generated

DOI: 10.1038/s41539-026-00449-z

Keywords: research culture, open science, knowledge sharing, research evaluation, research assessment, community engagement, reproducibility, npj Science of Learning, academic incentives, citizen science, narrative CV, meta-science

The research team, led by Tetsuji Minami of the National Cancer Center Japan alongside collaborators from several Japanese institutions, broke the 8.6 percent figure into distinct categories: 4.9 percent identified as gay or lesbian, 1.1 percent as bisexual, 1.0 percent as asexual or unsure of their attraction, and 1.6 percent as gender-diverse, meaning their current gender identity did not match the sex assigned to them at birth or fell outside the binary altogether. These estimates align with the 7.0 to 16.8 percent range reported in earlier Japanese studies conducted by private companies, government agencies, and sociologists, but earlier efforts suffered from convenience sampling, inadequate statistical adjustment, and narrow cohorts that limited their generalizability. By contrast, the new study calibrated its web-based sample against the Japanese government’s 2019 Comprehensive Survey of Living Conditions using a statistically demanding technique called inverse probability weighting.

The technical machinery behind the estimates deserves attention because it addresses a well-known weakness of internet surveys. Participants were drawn from a panel of 2.3 million people managed by Rakuten Insight, with stratified multistage sampling by age and sex, and a response rate of 65.7 percent among those invited. The researchers then applied two pre-set algorithms to screen out inattentive respondents, or “satisficers,” who straight-line their answers or fail directed attention checks, removing 9 percent of the raw sample. Sampling weights were predicted from a logistic regression model comparing the web survey respondents with the census-calibrated national survey, adjusting for area of residence, marital status, education, housing tenure, self-rated health, and smoking status. The resulting inverse probability weights allowed the team to produce population-level estimates with 95 percent confidence intervals, computed with a robust variance estimator, treating the findings explicitly as weighted distributions rather than causal effects, in line with modern descriptive epidemiology frameworks.

Measurement of sexual orientation and gender identity followed the internationally recommended “two-step approach,” which asks separately about sex assigned at birth and current gender identity, supplemented by a romantic and sexual attraction item to classify sexual orientation among cisgender respondents. Gender-diverse respondents were deliberately not reclassified by sexual orientation, and the authors caution that the gender-diverse category aggregates heterogeneous subgroups, including transgender, non-binary, and questioning individuals, because the survey instrument could not capture finer self-descriptions. Similarly, respondents who selected neither or unsure on the attraction item were grouped under an inclusive “asexual cisgender or unsure” label, an operationalization the authors urge readers to interpret cautiously since it may blend true asexuality with ongoing questioning. Inattentive respondents who reported anything other than male or female for assigned sex at birth were excluded as quality controls, consistent with Japan’s family register system, and were not intended to exclude minority identities.

Beyond headline prevalence, the study mapped striking demographic and socioeconomic patterns. All sexual and gender minority groups were younger on average than heterosexual respondents, with mean ages of 44.7 years for gay and lesbian participants, 38.3 for bisexual, 40.0 for asexual, and 38.4 for gender-diverse respondents, compared with 48.8 for heterosexual participants, and prevalence was concentrated under age 30. Gay respondents were more often male, while bisexual and asexual respondents were more often female, and gender-diverse respondents were evenly split by assigned sex. Marriage and partnership rates told a more complex story: only 47.7 percent of gay and lesbian respondents and 38.1 percent of gender-diverse respondents were married or partnered, versus 65.1 percent of heterosexual respondents, and a substantial share of gay and lesbian participants reported opposite-sex marriages or partnerships, a pattern the authors link to Confucian family norms emphasizing marriage and procreation, the absence of legal same-sex marriage recognition, and structural stigma.

Socioeconomic disparities emerged with unusual clarity because the national calibration allowed income, education, employment, insurance, and housing comparisons. Lower household equivalent income was more common among minority groups, particularly male sexual minorities and female gender-diverse respondents, and women had lower incomes than men within every category. Educational attainment diverged sharply from Western patterns: whereas studies in the United States and Europe generally find gay and bisexual men more educated than heterosexual men, Japanese gay, lesbian, and bisexual male respondents showed the opposite trend, with heterosexual men most likely to hold degrees. Employment differences also inverted expectations in places, with female sexual minorities more likely to be employed than heterosexual women, plausibly because married Japanese women are often outside the labor force while minority women marry less often. Health insurance coverage was lower among minority respondents of both sexes, and housing tenure varied significantly only among men, with asexual men and lesbian women least likely to own homes.

Health indicators painted a mixed picture that partially contradicts Western literature. Psychological distress, measured with the Kessler-6 scale, was consistently more common among minority respondents of both assigned sexes, echoing minority stress theory, which holds that stigma and marginalization drive mental health disparities. Body mass index patterns were also distinctive: underweight was more common among female minority respondents, while overweight and obesity clustered among male bisexual and asexual respondents and female asexual respondents. Perhaps most surprising, current smoking and habitual drinking showed limited or reversed differences, with minority groups tending to drink less than heterosexual respondents, contrary to consistent findings from England, Canada, and the United States. Substance use other than tobacco and alcohol was higher among minority respondents but remained rare overall. Medical comorbidity did not differ significantly once results were stratified by assigned sex at birth, and self-rated health differences disappeared under stratification, prompting the authors to call for future studies with covariate adjustment and clinical data linkage.

The significance of these findings extends well beyond Japanese borders. Until now, virtually all nationally representative data on sexual and gender minority populations came from Western countries, including the United States, the Netherlands, Sweden, Portugal, Canada, and the United Kingdom, creating a geographic evidence gap the authors argue reflects both research neglect and, in parts of Asia, legal environments where same-sex acts remain criminalized. Japan presents a distinctive hybrid context: same-sex relationships were historically tolerated within certain cultural traditions, cross-national analyses suggest relatively low structural stigma and low non-disclosure, yet Japan remains the only G7 nation without comprehensive anti-discrimination legislation or marriage equality. The study’s documentation of opposite-sex marriages among gay and lesbian respondents, observed at levels between the low Western figures of 3 to 9 percent and the much higher Chinese figures of 33 to 51 percent, offers rare quantitative grounding for understanding how family norms and legal structures shape minority lives in East Asian settings.

The authors draw direct policy implications from their data. They recommend that national administrative surveys routinely incorporate sexual orientation and gender identity items and household modules that recognize diverse partnership structures, enabling ongoing surveillance of socioeconomic disparities and targeted resource allocation. They call for accessible mental health support and anti-discrimination efforts in schools and workplaces to reduce the psychological burden the Kessler-6 results document, and for longitudinal and panel designs that oversample small subgroups and analyze intersections of age and assigned sex. Acknowledging limitations, including self-reported data, potential social desirability bias, residual web-survey selection bias that weighting could reduce but not eliminate, and the coarse aggregation of gender-diverse subgroups, the team nevertheless positions the work as a foundational step. With an estimated 8.6 percent of Japan’s population identifying as a sexual or gender minority, the study transforms a population previously invisible in national statistics into a measurable, monitorable constituency whose health and socioeconomic well-being can now be tracked, compared, and protected.

Subject of Research: National estimation of the proportion and characteristics of sexual and gender minorities in Japan using a nationwide cross-sectional internet survey

Article Title: Estimation of Sexual and Gender Diversity Among People Aged 15 Years and Older in Japan: A Descriptive Epidemiological Study Using a Nationwide Cross-Sectional Internet Survey

Article References: Minami, T., Inoue, M., Matsushima, M., Yoshioka, T., & Tabuchi, T. (2026). Estimation of Sexual and Gender Diversity Among People Aged 15 Years and Older in Japan: A Descriptive Epidemiological Study Using a Nationwide Cross-Sectional Internet Survey. Archives of Sexual Behavior. https://doi.org/10.1007/s10508-026-03515-0

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03515-0

Keywords: sexual orientation, gender identity, sexual and gender minorities, Japan, descriptive epidemiology, national survey, LGBTQ, health disparities, minority stress, inverse probability weighting, bisexual, transgender

The central question addressed by the study is deceptively simple: does the level of a nation’s productive capacity condition the extent to which gender inclusion translates into better governance? Productive capacity, in the economic literature, refers to the combination of resources, infrastructure, technological capability, institutional quality, and human capital that determines what an economy can produce and how efficiently it can do so. Economies with high productive capacity tend to have diversified industrial bases, reliable energy and transport systems, and educated workforces, while low-capacity economies often depend on narrow resource extraction and face persistent institutional constraints.

The theoretical logic connecting these concepts runs in both directions. On one side, stronger governance—characterized by rule of law, government effectiveness, regulatory quality, control of corruption, and accountability—can expand opportunities for women by enforcing anti-discrimination protections, opening access to education and credit, and creating formal channels for political participation. On the other side, greater gender inclusion can improve governance outcomes by diversifying the perspectives represented in decision-making bodies, reducing corruption risks, and aligning public policy more closely with the needs of the full population rather than a narrow subset.

Productive capacity enters this relationship as a potentially decisive moderating factor. Where economies possess the infrastructure and institutional depth to convert inclusion into concrete participation—through functioning labor markets, accessible education systems, and administrative machinery capable of implementing policy—gender inclusion is more likely to produce measurable governance gains. In settings where productive capacity is weak, formal inclusion may remain largely symbolic, with women represented in institutions that lack the resources or authority to effect change.

This distinction matters for policymakers because it suggests that promoting gender inclusion in isolation may yield disappointing results if the underlying economic and institutional substrate is underdeveloped. Conversely, investments in productive capacity—industrial diversification, digital infrastructure, energy reliability, and skills development—may amplify the returns to inclusion policies such as gender quotas, leadership training, and legal reforms. The study’s framing implies that the two agendas are complements rather than substitutes: capacity builds the stage on which inclusion can perform.

The research sits within a broader empirical literature in development economics and political science that has documented correlations between women’s political representation and outcomes such as reduced corruption, greater investment in public goods like health and education, and more durable peace agreements. Cross-national studies have repeatedly found that higher shares of women in legislatures are associated with stronger governance indicators, although establishing causality remains methodologically challenging because gender norms, income levels, and institutional quality tend to move together over time.

By introducing productive capacity as a moderating variable, the study offers a way to account for heterogeneity in these findings. Countries at similar income levels may differ sharply in their structural capabilities, and the same inclusion policy may therefore produce different results depending on whether the surrounding economy can absorb and institutionalize the change. This perspective aligns with structuralist traditions in development economics, which emphasize that outcomes depend not only on policy choices but on the productive structure of the economy itself.

The publication appears in Humanities and Social Sciences Communications, an open-access journal published by Springer Nature that publishes peer-reviewed research spanning the social sciences and humanities. The article carries the DOI 10.1038/s41599-026-08982-3 and is available through the journal’s website. As with all work in this field, the findings invite further empirical testing across country samples, time periods, and measurement approaches, particularly as new datasets on economic complexity and institutional performance become available.

For researchers, the study underscores the value of interaction-based designs that test not just whether two variables are related, but under what conditions that relationship strengthens or weakens. For practitioners in international development and governance reform, the message is that gender inclusion strategies are most likely to succeed when they are embedded in broader programs of capacity building—ensuring that the institutions women join are equipped to deliver the improvements that inclusive governance promises.

One useful way to situate the study is within the long-running debate over how to measure both of its core constructs. Gender inclusion is typically operationalized through indicators such as female labor force participation, seats held by women in national parliaments, educational attainment gaps, and indices that composite these dimensions into a single score. Each measure captures a different facet of inclusion: parliamentary representation reflects formal political voice, while labor force participation reflects economic integration. Because these dimensions do not always move together—a country may have high female employment alongside minimal political representation—researchers must be explicit about which aspect of inclusion they are testing, and the moderating role of productive capacity may differ across them.

Productive capacity presents its own measurement challenges. Recent work by international organizations has developed composite indices that combine measures of energy infrastructure, transport and digital connectivity, structural change away from primary commodities, and the sophistication of exported goods. A related strand of literature on economic complexity infers productive knowledge from the diversity and ubiquity of the products a country exports, treating the export basket as a window into the accumulated capabilities of its firms and workforce. These approaches share the premise that what an economy can do is distinct from how much income it currently generates, which is precisely the distinction the study leverages when it argues that capacity, rather than income alone, conditions the governance returns to inclusion.

The historical record of gender quota adoption offers a concrete illustration of why this conditioning matters. Well over one hundred countries have now introduced legislative quotas of some form, ranging from reserved seats to voluntary party quotas, and the resulting variation in outcomes is striking. In some settings, quotas produced durable cohorts of legislators who reshaped committee agendas and oversight practices; in others, women entered chambers with limited committee assignments, short tenures, or weak party support, and the anticipated governance effects failed to materialize. Observers of these divergent trajectories frequently point to features that resemble productive capacity in the study’s sense: the professionalism of the civil service, the reliability of public administration, and the broader economic base that gives legislatures real resources to allocate.

The corruption channel deserves particular attention because it has generated one of the most cited empirical regularities in this literature. Cross-national analyses have repeatedly reported that higher shares of women in parliament are associated with lower perceived corruption, and early interpretations framed this as evidence that women are inherently less tolerant of corrupt exchanges. Subsequent scholarship has complicated that reading, suggesting that the relationship may reflect the institutional environments in which women gain office rather than any dispositional difference. Liberal democracies with strong rule of law both elect more women and exhibit less corruption, making it difficult to isolate the effect of gender itself. A moderating variable such as productive capacity offers a more structural interpretation: the same woman legislator operates within very different opportunity structures depending on the administrative and economic machinery surrounding her office.

There is also a macroeconomic dimension worth noting. Growth research over the past two decades has emphasized that sustained convergence depends on structural transformation—the reallocation of labor from low-productivity agriculture toward manufacturing and services—rather than on factor accumulation alone. If gender inclusion contributes to governance, and governance contributes to the quality of public investment and the enforcement of property rights, then the interaction the study examines may feed back into the very productive capacity that conditions it. This possibility of circular causation is a familiar challenge in development research, and it cautions against reading any single estimated coefficient as a complete description of the system.

Methodologically, studies of this kind typically rely on panel data covering many countries and years, with interaction terms used to test whether the slope linking inclusion to governance varies with the level of capacity. Such designs face well-known hurdles: omitted variables that correlate with both capacity and governance, reverse causality running from institutional quality to economic structure, and the sensitivity of results to sample composition and estimator choice. Robustness in this literature is usually assessed by varying measures, excluding regions, and testing alternative governance indices drawn from expert assessments, survey-based perceptions, and objective indicators. Readers interpreting the findings should keep in mind that interaction effects in cross-national panels are notoriously sensitive to these choices, even when the underlying theory is sound.

The policy translation also requires care. If capacity amplifies the returns to inclusion, then sequencing becomes a live question: should donors and governments invest in industrial and administrative capability first, or pursue inclusion and capacity simultaneously? The complementarity framing suggests simultaneous pursuit, since waiting for capacity to mature before opening political space risks entrenching exclusion, while pursuing inclusion without capacity risks symbolic representation. Practical programs that pair quota reforms with civil service professionalization, digital government infrastructure, and vocational education embody this logic, though rigorous evaluations of such paired interventions remain scarce.

Finally, the study contributes to a growing effort to bring structural economics and political sociology into a single analytical frame. Much of the governance literature has treated institutions as exogenous rules to be transplanted, while much of the gender literature has treated norms as the primary constraint. By foregrounding the productive structure of the economy as the terrain on which both rules and norms operate, the research points toward an integrated account in which economic capabilities, social inclusion, and institutional quality co-evolve—a perspective likely to inform the next generation of empirical work on development outcomes.

Subject of Research: The moderating role of productive capacity in the relationship between gender inclusion and governance quality.

Article Title: Gender inclusion and governance: the role of productive capacity

Article References: Gender inclusion and governance: the role of productive capacity. (n.d.). https://doi.org/10.1038/s41599-026-08982-3

Image Credits: AI Generated

DOI: 10.1038/s41599-026-08982-3

Keywords: gender inclusion, governance, productive capacity, political participation, institutional quality, development economics, women in politics, economic complexity, public policy, corruption, human capital, Social Sciences Communications

The research, led by Lei Li and Zhiqing Li of the State Key Laboratory of Lithospheric and Environmental Coevolution at the Institute of Geology and Geophysics, Chinese Academy of Sciences, together with colleagues from Zhejiang Jiuhe Geological Ecological Environment Planning and Design and China University of Mining and Technology, began with an exhaustive inventory of the region’s slope failures. Combining satellite remote sensing interpretation with painstaking field investigation, the team documented 439 landslides covering a total area of 16.85 square kilometers along the Lhasa–Dingri section of the highway. That catalog is more than a tally of past disasters; it is the training ground on which any predictive model must learn, and its quality determines whether the resulting maps can be trusted.

With the inventory in hand, the researchers turned to the question that has long puzzled geoscientists working in this region: which factors actually control where landslides occur? The answer, the study finds, is written in the topography. Slope angle and local topographic relief emerged as the dominant controls on landslide distribution, outweighing other candidate variables. This is no accident of local geography but the signature of a continent-scale tug of war. The Indo-Eurasian collision continues to push the Tibetan Plateau upward, while the Yarlung Tsangpo River and its tributaries cut downward with equal ferocity, carving deeply incised valleys and steepening hillsides faster than erosion can mellow them. The result is a landscape primed for failure, where the steepest slopes and the greatest local relief concentrate landslide activity along the very corridors engineers have carved out for roads.

Understanding those causal mechanisms is only half the battle. The other half is building a model that predicts susceptibility without fooling itself, and this is where the study makes its most consequential methodological contribution. Landslide susceptibility mapping has become a showcase application for machine learning, with random forests, support vector machines, and neural networks routinely churning out impressive-looking hazard maps. Yet the team identified a systemic flaw in how these models are typically evaluated. Most studies split their data into training and testing sets at random, which sounds neutral but is quietly misleading in a spatial setting.

The problem is spatial autocorrelation. Landslides cluster: a slope that failed once is surrounded by neighbors with nearly identical elevation, aspect, lithology, and drainage, and those neighbors are likely to fail too. When a random split places some of these near-duplicate points in the training set and their twins in the test set, the model is effectively being quizzed on answers it has already memorized. The resulting performance scores are over-optimistic, flattering the algorithm while overstating its true ability to generalize to unfamiliar terrain. For a highway engineer deciding which slopes to reinforce, that flattery can be dangerous.

To break this circularity, the researchers built their random forest framework around a rigorous sixfold spatial cross-validation scheme. Instead of shuffling individual points, the study area is partitioned into spatially distinct blocks, and the model is trained on some blocks and tested on entirely separate ones. Each fold therefore simulates the real-world challenge the model will face: predicting hazard in terrain it has never seen. The team paired this validation strategy with Bayesian hyperparameter optimization, using Gaussian-process-based search to efficiently tune the random forest’s settings rather than relying on default values or brute-force grid searches. The combination yields a model whose reported accuracy reflects genuine predictive skill rather than geographic leakage.

The payoff was clear in the final susceptibility maps. The spatially cross-validated model demonstrated superior generalization compared with conventional approaches, producing hazard estimates that held up when confronted with new territory. The maps reveal that sections of the G318 corridor passing through deeply incised valleys, steep slopes, and areas of high local relief carry the highest landslide susceptibility, and the authors flag these segments as requiring the most attention from maintenance and protection programs. In a region where a single road-blocking landslide can isolate communities and disrupt a strategic artery, knowing precisely which kilometers of highway deserve priority investment is a matter of practical consequence, not academic refinement.

The study’s implications reach well beyond one highway. Susceptibility models are now standard tools in hazard zoning and land use planning worldwide, and the over-optimism problem the team documented is endemic to the field. By demonstrating that spatial cross-validation changes the picture of model reliability, the researchers add weight to a growing consensus that validation design, not just algorithm choice, determines whether a susceptibility map is science or decoration. Their framework, which couples a carefully constructed landslide inventory with Bayesian-tuned random forests and block-based validation, offers a template that other teams working along the Himalayan arc, the Sichuan–Tibet corridor, and other tectonically active mountain belts can adapt directly.

There is also a deeper geoscience lesson embedded in the results. The finding that slope and local relief dominate landslide occurrence ties the modern hazard map to the long-term evolution of the plateau itself, where tectonic uplift and fluvial incision jointly set the tempo of erosion. In that sense, the machine learning model is not merely a predictive device but a diagnostic instrument, revealing in statistical form the geomorphic engine that has been shaping the southern Tibetan margin for millions of years. As climate change alters precipitation patterns and infrastructure expansion pushes roads into ever more precarious terrain, tools of this kind, honest about their own uncertainty and grounded in the physical mechanisms of failure, will become indispensable for keeping mountain lifelines open.

Subject of Research: Landslide susceptibility mapping along the Lhasa–Dingri highway corridor on the Tibetan Plateau using spatially cross-validated random forest modeling

Article Title: Optimizing landslide susceptibility mapping using spatially cross-validated random forest: Lhasa–Dingri corridor, Tibetan Plateau

Article References: Li, L., Li, Z., Qi, Z., Su, W., Sun, K., Wang, S., Kong, Y., & Hu, R. (2026). Optimizing landslide susceptibility mapping using spatially cross-validated random forest: Lhasa–Dingri corridor, Tibetan Plateau. Natural Hazards, 122(19), Article 635. https://doi.org/10.1007/s11069-026-08374-5

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08374-5

Keywords: landslide susceptibility, random forest, spatial cross-validation, Tibetan Plateau, machine learning, G318 National Highway, topographic controls, remote sensing, Bayesian optimization, geomorphology, natural hazards, Yarlung Tsangpo River

The study was carried out between September 2025 and January 2026 in government-run shelters located in a low-resource region of eastern Iran. Researchers recruited thirteen homeless women through a combination of convenience and purposive sampling, a strategy that allowed them to capture both readily accessible participants and women with particular experiences relevant to the research question. Each participant took part in a semi-structured, in-depth interview that was audio-recorded and later transcribed verbatim. The interviews explored how the women perceived the conditions, circumstances, and structural forces that influenced their day-to-day health, from the availability of food and shelter to their interactions with health services and the communities around them.

To analyze the transcripts, the team applied a manual thematic analysis following Boyatzis’s methodological approach, a well-established technique in qualitative health research that involves systematically coding data and clustering codes into themes. The researchers used the Healthy People 2030 framework, developed by the United States Department of Health and Human Services, as their conceptual guide. This framework organizes social determinants of health, sometimes called social drivers of health, into five domains: Economic Stability, Education Access and Quality, Health Care Access and Quality, Neighborhood and Built Environment, and Social and Community Context. Anchoring the analysis in this framework allowed the Iranian findings to be compared with a global body of research on how social conditions shape health outcomes.

The analysis revealed twenty-four social determinants of health distributed across the five framework themes. Two domains stood out for their weight in the women’s lives: Economic Stability and Social and Community Context. These two themes encompassed the highest number of determinants, underscoring, the authors argue, their influential role in the ultimate health outcomes of homeless women. In practical terms, this means that the women’s health was shaped less by individual clinical risk factors alone and more by the grinding arithmetic of poverty, income insecurity, and the presence or absence of supportive relationships and community ties. For a population with no stable housing, economic shocks translate directly into skipped meals, untreated illness, and exposure to unsafe environments.

The emphasis on Economic Stability reflects conditions specific to the border region studied. Eastern Iran’s provinces adjacent to Afghanistan are characterized by pronounced socioeconomic and healthcare disparities, and they host communities affected by migration flows, unemployment, and limited public infrastructure. Homeless women in this setting face a compounding set of disadvantages: they are women in a context of gendered economic exclusion, they are homeless in a region with thin social services, and many are affected by the broader instability associated with the Afghan border. The study’s findings suggest that any intervention aimed at improving these women’s health must begin with economic levers, including income support, employment pathways, and reliable access to basic material needs.

The second dominant domain, Social and Community Context, points to the relational side of health that is often overlooked in clinical settings. For homeless women, social isolation, stigma, family breakdown, and the absence of trusted support networks can be as damaging to health as any pathogen. The interviews captured how the women navigated relationships with shelter staff, other residents, family members, and the wider community, and how the quality of those relationships influenced their willingness to seek care, their mental wellbeing, and their sense of dignity. The researchers note that these findings hold particular value for low-resource settings, where formal health systems cannot alone compensate for the absence of social support structures.

The remaining three themes of the Healthy People 2030 framework also yielded concrete determinants. Education Access and Quality captured how limited literacy and schooling constrained the women’s ability to find work, understand health information, and advocate for themselves. Health Care Access and Quality encompassed barriers such as cost, distance, documentation status, and experiences of discrimination when seeking treatment. Neighborhood and Built Environment covered the physical conditions of shelters and streets, including safety, sanitation, and exposure to environmental hazards. Together, the twenty-four determinants form what the authors describe as a comprehensive list of social determinants of health for homeless women, one of the most granular portraits assembled for this population in the region.

Methodologically, the study demonstrates the value of qualitative inquiry in settings where quantitative data on homeless populations are scarce or unreliable. Homeless women are frequently missed by censuses, surveys, and routine health information systems, meaning their needs remain statistically invisible even as their suffering accumulates. By sitting down with thirteen women and systematically coding their accounts, the research team surfaced determinants that would never appear in administrative datasets. The use of a recognized international framework strengthens the study’s utility, allowing local findings to inform global conversations about housing, health equity, and the social drivers of disease, while remaining grounded in the specific realities of Iran’s eastern borderlands.

The authors conclude that the study highlights the pivotal role of governments in addressing the health of homeless women. Because the determinants identified span income, education, healthcare, housing, and community life, no single ministry or program can tackle them in isolation. Effective responses, the findings imply, require coordinated policy action: social protection schemes that reach women without fixed addresses, shelters that connect residents to healthcare and education, anti-discrimination measures in health facilities, and investment in the infrastructure of neglected border regions. The research was funded by Birjand University of Medical Sciences and approved by the university’s ethics committee, with informed consent obtained from all participants and their anonymity strictly protected throughout collection and analysis.

Beyond its immediate regional significance, the study offers a template for understanding homelessness as a public health issue rather than merely a social welfare problem. The twenty-four determinants mapped by the researchers illustrate how health is produced, or eroded, long before a patient reaches a clinic, in the labor market, the classroom, the neighborhood, and the family. For the homeless women of Iran’s Afghan border regions, the path to better health runs through economic security and human connection as much as through medicine. The study’s comprehensive inventory gives policymakers, clinicians, and advocates a concrete starting point, and it gives a long-invisible population something it has rarely been granted: a documented, systematic account of the forces shaping their lives.

Subject of Research: Social determinants of health among homeless women in Iran's border regions with Afghanistan

Article Title: Social determinants of health among homeless women in border regions of Iran with Afghanistan: a qualitative study

Article References: Khosravi, M., Khosravi, F., Mohammadi, F., Rezaei Qazravan, Z., & Hajiaghaye, Z. (2026). Social determinants of health among homeless women in border regions of Iran with Afghanistan: a qualitative study. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00470-y

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00470-y

Keywords: social determinants of health, homeless women, Iran, Afghanistan border, qualitative study, Healthy People 2030, economic stability, healthcare access, shelters, health equity, Birjand University of Medical Sciences, public health