In a groundbreaking new study published in 2025, researchers have harnessed the power of machine learning to dissect and measure the representation of women in science, technology, and innovation policy (STIP). This innovative research not only challenges long-held assumptions about diversity initiatives like quota systems but also reveals deep-seated institutional obstacles that continue to impede women’s progress in these critical fields. By deploying sophisticated computational methods alongside institutional theory frameworks, the study provides unprecedented insights into the nuanced dynamics shaping gender parity in STEM-related policymaking environments.
One of the most striking findings overturns conventional wisdom: countries lacking formal diversity and inclusion quota systems (DIQS) often outperform those with established quotas in terms of women’s representation in STIP. Statistical analyses show that nations without quotas posted median and mean representation scores around 0.3675 and 0.3676 respectively—surpassing the figures recorded in quota-enforced contexts. This counterintuitive discovery has profound implications, questioning the effectiveness and implementation quality of mandated numerical targets. The research suggests that quotas, when deployed as isolated formal mechanisms, frequently collide with entrenched informal rules and cultural norms that govern organizational behavior, limiting their real-world impact.
The researchers frame their analysis through the lens of institutional theory, distinguishing sharply between formal and informal institutions. Whereas quotas represent explicit, codified policies, informal institutions encompass unwritten societal expectations, tacit organizational habits, and ingrained cultural standards. The study illuminates how the mere presence of quotas falls short unless accompanied by meaningful shifts in these informal arrangements—the “logic of appropriateness” deeply embedded in policy realms. Such findings imply that sustainable progress in gender equity calls for comprehensive, multi-dimensional strategies incorporating mentorship, leadership training, and inclusive organizational reforms alongside formal mandates.
Delving further, the study examines cross-sector dynamics, revealing a startling disconnect between women’s participation in STEM fields and their ascension to policymaking leadership roles. The statistical correlation between Women in STEM Participation (WSP) and Women in Policymaking Leadership Roles (WPLR) registers at a mere 0.11, exposing a significant rupture in the talent pipeline that impedes women’s transition from technical expertise to positions of policy influence. Yet, a contrastingly strong correlation of 0.73 between women’s general policymaking participation and leadership roles suggests that once women enter policymaking sectors, advancement mechanisms tend to function more effectively within those domains.
This pipeline gap underscores persistent institutional barriers that stymie women’s ability to translate STEM qualifications into policymaking clout. Structural gender biases, organizational segmentation, and cultural boundaries delineate rigid lines between technical and policy spheres, limiting individual agency despite specialized knowledge. The research points towards targeted interventions—cross-sector mentorship, leadership development programs, and formal engagement pathways—as vital to bridging this divide and unlocking the full potential of female STEM experts within policy spaces.
Behind these empirical revelations lies the research’s advanced methodological framework, which leverages Support Vector Regression (SVR) combined with K-Nearest Neighbors (KNN) imputation and autoencoder feature engineering to address missing data and capture complex variable interactions. The SVR model achieved a remarkable R² score of 0.8350 with minimal error metrics, outperforming or matching traditional linear regression and ridge models. This capacity for nuanced pattern recognition enables the study to factor in diverse institutional, cultural, and structural variables, positioning machine learning as a powerful tool for social science research on gender representation.
Crucially, the study emphasizes that institutional culture—beyond formal quota policies—plays a decisive role in shaping women’s participation in STIP sectors. Organizational climates that support diversity and inclusive decision-making significantly enhance the effectiveness of gender initiatives. The findings advocate for mandatory bias training, transparent promotion criteria, regular pay equity audits, and inclusion metrics embedded in performance evaluations to create environments conducive to female talent retention and advancement. This cultural transformation reflects a necessary paradigm shift in how institutions approach diversity beyond numerical targets alone.
The researchers also highlight the importance of educational pipelines in sustaining long-term gender equity gains. Early interventions aimed at girls’ STEM engagement, equitable access to scholarships and internships, and outreach programs constitute critical priorities. Strengthening support systems across educational and career stages enables smoother transitions between STEM and policy careers, helping to cultivate a robust pipeline of female leaders equipped to navigate complex, multi-sector governance environments.
Addressing the broader theoretical implications, the study advances institutional theory by empirically demonstrating the interplay between formal policies and informal institutional norms in determining women’s representation outcomes. The weak correlation between STEM participation and policymaking leadership validates notions of institutional segmentation and gendered organizational fields with distinct legitimacy criteria. By integrating feminist institutionalism, the research reveals how ostensibly gender-neutral policies are often neutralized by embedded gendered practices, generating resistance despite formal commitments to gender parity.
On the methodological front, this study distinguishes itself by adopting hybrid machine learning and advanced imputation techniques rarely applied in gender-focused social science investigations. Complementing recent research on innovation disparities in academic publishing, it builds a computational infrastructure capable of robustly quantifying complex gender representation phenomena within policy ecosystems. These methodological advances open new frontiers for future research while underpinning the study’s highly accurate predictive modeling.
Despite its strengths, the research acknowledges limitations related to data heterogeneity, cultural variability, and binary gender categorizations. Reliance on secondary data from multiple international databases introduces measurement variance and potential biases, while cultural factors—such as social values on gender equality and localized informal norms—may elude complete capture by the models. The authors emphasize the need for future studies to incorporate intersectional analyses, longitudinal designs, and expanded gender frameworks to more fully unravel the complexities surrounding women’s engagement in STIP.
Culturally, the success of countries without formal quotas likely reflects profound informal institutional environments fostering women’s inclusion organically. Such environments underscore the significance of context-specific cultural values, historical leadership precedents, and societal norm conformity in mediating policy impacts. The findings caution against overgeneralizing quota effectiveness and call for tailored approaches sensitive to national and sectoral idiosyncrasies.
Looking ahead, the study outlines crucial avenues for further investigation. Long-term tracking of intervention outcomes, intersectional assessments across race and socioeconomic status, and expanded research on gender minorities in governance promise to enrich understanding and policy design. Researchers are encouraged to explore how informal institutional arrangements adapt to formal policies, using longitudinal methodologies to reveal the dynamic feedback loops shaping gender equity trajectories.
To translate these findings into tangible progress, the authors recommend comprehensive multi-layered strategies. Organizations must invest in leadership development programs bridging STEM and policymaking sectors, emphasizing formalized knowledge transfer mechanisms like joint working groups and cross-functional teams. Robust monitoring and evaluation infrastructures are essential to identify challenges, assess program efficacy, and recalibrate diversity initiatives iteratively.
Further, cultural transformation must accompany structural reforms. Mandatory bias mitigation training for all employees—especially leadership—and the incorporation of diversity metrics in performance reviews foster an inclusive culture. Transparent compensation and promotion policies, coupled with regular pay equity audits, ensure fair treatment and signal organizational commitment to gender parity.
Collaborations with academic institutions to fortify educational pipelines remain imperative. Early outreach, internships, and targeted recruitment strategies tailored to dismantle entry barriers will expand the pool of female STEM talent progressing eventually into policymaking realms. Strong partnerships with professional associations supporting women across both technical and policy fields amplify these efforts synergistically.
In conclusion, this pioneering study reframes gender representation in STIP by unveiling how deeply embedded institutional factors influence outcomes beyond formal policy overlays like quotas. It underscores the critical need for intertwined strategies that simultaneously address structural, cultural, and educational dimensions fostering women’s full participation and leadership. The integration of cutting-edge machine learning with robust theoretical frameworks not only produces precise, scalable measurement tools but also illuminates pathways toward more effective, context-sensitive interventions. As gender equity remains a global priority, this research sets a benchmark for future scholarship and policy innovation aimed at dismantling systemic barriers and nurturing inclusivity within science, technology, and policy arenas.
Subject of Research: Women’s Representation in Science, Technology, and Innovation Policy (STIP)
Article Title: Applying machine learning to gauge the number of women in science, technology, and innovation policy (STIP): a model to accommodate missing data.
Article References:
Meyer, C., Baogui, D. & Gouda, M.A. Applying machine learning to gauge the number of women in science, technology, and innovation policy (STIP): a model to accommodate missing data. Humanit Soc Sci Commun 12, 1245 (2025). https://doi.org/10.1057/s41599-025-05610-4
Image Credits: AI Generated
