At the heart of Yang’s book is a systematic comparison of how Chinese and Anglo-American societies conceive the public good of higher education. Using a method the review describes as trans-positional analysis, Yang moves between the two traditions across five key themes, refusing to treat either as a universal benchmark against which the other must be measured. In the Anglo-American tradition, the public good of universities has been framed largely through liberal individualism, market accountability and the state’s retreat from direct provision. In the Chinese tradition, by contrast, education has long been entwined with the moral cultivation of persons, the responsibilities of family and collectivity, and the state’s role as guarantor of social harmony and meritocratic mobility. Yang’s argument is that these are not merely different policy preferences but structurally different ways of generating public value.

The reviewers sharpen this point by distinguishing three levels at which the structural differences operate. At the normative level, the two traditions answer differently what a university is for: individual enrichment and economic productivity in the Anglo-American frame, versus self-perfection and collective flourishing in the Confucian frame. At the institutional level, they differ in how funding, governance and evaluation distribute responsibility between state, market and society, with Chinese higher education embedding the state more deeply in defining educational purpose, as seen in debates over the gaokao entrance examination and its double constraints. At the subjective level, students and academics internalise these divergent logics, shaping how individuals imagine their own agency, obligation and belonging within the university.

What makes the review—and the book it examines—technically significant for the field is the concept of epistemic justice, borrowed from Miranda Fricker’s 2007 work on power and the ethics of knowing. Applied to higher education research, epistemic injustice means that knowledge produced in non-Western traditions is systematically discounted, translated into Western categories or treated as exotic raw material rather than as theory in its own right. Tao and Li argue that Yang’s comparison directly challenges this Western-centrism. By drawing on Chinese philosophical resources with the same seriousness conventionally reserved for liberal political theory, the book performs what the reviewers call an advance in epistemic justice: it insists that the Chinese tradition can generate concepts, not merely case studies.

The most conceptually charged of these resources is tianxia, often translated as all-under-heaven, an classical Chinese vision of world order in which political and moral community extends across the entire known world rather than being bounded by nation-states. In recent years, philosopher Zhao Tingyang has revived tianxia as an ontological argument for a new world order, and Yang and her collaborators—including Simon Marginson and Xin Xu—have developed it as a heuristic for higher education, proposing a world-centred rather than nation-centred imaginary for global academia. Their 2024 article in Globalisation, Societies and Education, titled Thinking through the world, and their 2025 Higher Education paper on the global aspirations of Chinese universities both treat tianxia as a lens for imagining universities as contributors to a shared human world rather than competitors in a zero-sum rankings game.

Yet the review is refreshingly candid about the disputes this agenda has provoked, and it engages critics rather than dismissing them. Four risks receive sustained attention: reductionism, essentialism, re-orientalism and the co-optation of tianxia. Reductionism is the danger of flattening vast, internally diverse civilisations into single explanatory formulas. Essentialism treats Chinese and Western cultures as fixed, homogenous essences rather than dynamic, hybridising formations—a concern echoed by W.W. Lo and Rui Yang’s work on hybridisation and recombination in Chinese societies, and by Zhu, Shen and Yang’s 2025 Higher Education study showing how Chinese humanities and social science scholars actively transform traditions into academic resources rather than merely inheriting them.

Re-orientalism poses a subtler trap. Building on Edward Said’s 1977 critique of Orientalism and subsequent debates in Chinese scholarship by scholars such as Wang Ning and Zhou Ning, the reviewers note that even well-intentioned efforts to celebrate Chinese traditions can reproduce the same East-West binary that Orientalism created, positioning China as a mystical civilisational Other. Mulvey’s 2026 article in Comparative Education goes further, asking whether the essentialisation of China within so-called critical internationalisation studies constitutes a new Orientalism. The tianxia concept attracts the sharpest critique of all: Fei Yan’s 2026 article, The darker side of Tianxia, warns that the framework carries imperial histories and could be co-opted to serve state power, while Moreno García and Pines’s comparative historical work on Maat and Tianxia reminds readers that ancient world-order ideologies, Egyptian and Chinese alike, were instruments of rule as much as philosophies of harmony. Berlin debates between Zhao and Western philosophers, documented in World Philosophy, show that these objections are not merely external attacks but live controversies within the tianxia literature itself.

The reviewers’ response to these disputes is arguably the review’s most important analytical move. Rather than concluding that tradition-based research is too dangerous or too essentialist to pursue, they argue that the risks are internal to any serious cross-cultural scholarship and must be managed through methodological reflexivity, attention to hybridity and genuine two-way dialogue. They situate Yang’s book within a broader movement documented across the field: Xu’s work on epistemic diversity in comparative research and on a Chinese definition of internationalisation; Bamberger, Mulvey and Yan’s 2026 call for internationalisation scholarship beyond the Western horizon; Jackson and Kwak’s probing question of whether philosophy of education is Western at all; and parallel non-Western resources such as Dladla’s Ubuntu philosophy of liberation and Mun and Min’s account of the Korean public good as jeong. Seen together, these works suggest a discipline in the midst of an epistemic diversification whose trajectory Yang’s book may well consolidate.

The practical stakes extend well beyond theory. For policymakers, the three-level framework—normative, institutional, subjective—offers a diagnostic tool for understanding why policy transplants so often fail: a governance model detached from its civilisational soil produces different, and sometimes perverse, effects when grafted onto another tradition. For university leaders navigating international partnerships, the review implies that cooperation grounded in mutual conceptual translation will be more durable than arrangements that quietly assume Anglo-American categories are universal. For students, particularly the growing cohort of globally mobile learners whose identity negotiations are documented in Xiaoshi Li’s own research on Chinese master’s students in Hong Kong, the debate determines whether their educational traditions are treated as liabilities to be overcome or as resources to be drawn upon.

Tao and Li close with a forward-looking assessment: Yang’s book, they conclude, will prompt more equal and meaningful cross-cultural dialogues in the field, making it invaluable to researchers, policymakers, students and anyone interested in what they call civilisational dialogues. The phrase is telling. What began as a book review has become a statement about the future epistemology of higher education research itself—a field long dominated by centres in the United States, the United Kingdom and Western Europe, and increasingly challenged by scholarship from East Asia and the Global South. Whether tianxia, Confucian self-cultivation and kindred concepts can enrich a genuinely global conversation without hardening into new orthodoxies remains an open question. But the review makes clear that the question can no longer be avoided, and that the answer will shape how universities everywhere understand their public purpose.

Subject of Research: Traditions as civilisational resources for cross-cultural dialogues in global higher education research

Article Title: Traditions as civilisational resources for cross-cultural dialogues in global higher education research: disputes and prospects

Article References: Traditions as civilisational resources for cross-cultural dialogues in global higher education research: disputes and prospects. (n.d.). https://doi.org/10.1007/s10734-026-01759-2

Image Credits: AI Generated

DOI: 10.1007/s10734-026-01759-2

Keywords: higher education, public good, cross-cultural dialogue, tianxia, epistemic justice, comparative education, Chinese tradition, Anglo-American higher education, Western-centrism, civilisational dialogue, Lili Yang, internationalisation

The ten systems examined were Argentina, Chile, Chinese Taipei, Finland, Hungary, Italy, Japan, Korea, the Philippines, and the United States. The authors stress that this was a purposive comparative sample, chosen to span geographic regions, OECD membership status, and a wide range of average performance, from a weighted mean mathematics score of 352.57 in the Philippines to 531.14 in Chinese Taipei. The design supports comparisons across these ten systems but does not license claims about all countries or continents. Within the sample, 2,617 schools contributed students, with country sample sizes ranging from 4,838 in the United States to 11,975 in Argentina.

What distinguishes this study from much of the educational data-mining literature is its fidelity to PISA’s assessment architecture. Mathematics achievement in PISA is not a single number but a set of ten plausible values, multiple imputed estimates that represent uncertainty in each student’s latent proficiency. The researchers fitted and evaluated every model across all ten plausible values rather than relying on just the first. They also incorporated the final student weight, W_FSTUWT, in descriptive statistics, model fitting, and test-set evaluation, and used the 80 student replicate weights to estimate sampling variability. Total variance for each performance metric combined the mean replicate-weight sampling variance with the between-plausible-value variance, following the formula U-bar plus (1 + 1/M) times B, so that confidence intervals reflected both complex sampling uncertainty and achievement-scaling uncertainty. Few machine-learning studies of PISA data go to these lengths.

The predictor side of the analysis was equally disciplined. The authors constructed a transparent primary candidate pool of 33 predictors from PISA 2018 student questionnaire variables and OECD-derived indices, organized into six substantive domains: student demographics, family socioeconomic status and home resources, engagement and learning time, peer climate, belonging and parent support, and classroom climate. Rather than assuming all 33 variables mattered everywhere, they performed country-specific stability feature selection using only training schools. Three complementary methods were applied: mutual information, which captures general dependence; weighted elastic net regression, a regularized linear method that handles correlated predictors through combined L1 and L2 penalties; and weighted random forest ranking, which captures nonlinear and interaction-based structure. Feature rankings were repeated across ten plausible values and five internal school-level folds, yielding 50 selection runs per country. A predictor was deemed stable if selected in at least half of those runs. The resulting stable sets contained between 11 and 15 predictors, averaging 14.0 per country.

Four models were then compared under country-specific school-level holdout evaluation, with roughly 70 percent of students in training and 30 percent in test sets. Splitting by school rather than by student reduced leakage from students in the same school appearing on both sides. The contenders were weighted linear regression, weighted linear regression augmented with pairwise interactions, random forest, and CatBoost, a gradient-boosted tree ensemble designed for structured tabular data. Averaged across countries, CatBoost was the clear winner, achieving a mean weighted R-squared of 0.358 and a mean mean absolute error of 57.29 score points. Random forest followed with a mean weighted R-squared of 0.313 and an MAE of 59.30. The interaction-augmented linear model barely improved on the additive linear benchmark, with mean R-squared values of 0.290 versus 0.286. CatBoost posted the lowest weighted MAE in all ten systems, and its advantage over random forest was consistent but moderate: roughly 2.01 MAE points and 0.045 in R-squared on average. Its country-level R-squared ranged from 0.210 in Italy to 0.443 in Hungary.

The interpretive core of the study used SHAP, or Shapley additive explanations, a technique that decomposes each model prediction into additive contributions from individual features. The authors were careful to frame SHAP summaries as model-based explanations of predictive associations, not causal effects. Because CatBoost performed best, interpretation focused on its SHAP values, computed on held-out test schools and averaged across all ten plausible values to produce PV-robust stability summaries. The headline finding is that books at home was the most stable predictor of all, ranking among the top ten SHAP predictors in all ten countries on average and among the top five in 8.7 countries on average. Highest parental occupational status was nearly as stable, appearing in the top ten in 9.2 countries and the top five in 7.1. Socioeconomic status itself appeared in all ten country-specific models and ranked in the top ten in 8.3 countries on average, though its average top-five count was lower at 4.3.

Beyond the socioeconomic core, the picture became more heterogeneous. Grade placement entered the stable feature sets of seven countries and ranked in the top ten in all seven, but it was not selected in Chinese Taipei, Japan, or Korea. Mathematics learning time appeared in six countries and ranked in the top ten in all six, with an average top-five count of 5.5, and it ranked especially highly in Chinese Taipei, Japan, and the United States. Test effort was retained in only four systems but was consistently influential there, ranking in the top five in all four and even first in Finland, Italy, and Korea in the diagnostic visualization. Directed instruction and disciplinary climate also showed cross-national reach, ranking in the top ten in 7.0 and 5.8 countries on average respectively. The authors argue that this mix of stability and country-specificity is precisely why a single pooled importance ranking would be misleading.

Sensitivity analyses reinforced the robustness of these conclusions. Comparing SHAP summaries based on the first plausible value with summaries averaged across all ten showed only small differences, with maximum absolute gaps of 0.8 countries for top-ten counts, 1.2 countries for top-five counts, and 1.25 rank positions for mean rank, confirming that the main pattern was not an artifact of PV1MATH. A second sensitivity analysis removed grade placement from the stable feature sets where present and refitted the CatBoost models. Performance dropped modestly, with a mean R-squared change of -0.028 and a mean MAE increase of 1.17 score points, the largest decline occurring in Chile, where R-squared fell by 0.091 and MAE rose by 3.92 points. Crucially, the SHAP stability pattern remained broadly similar without grade: books at home, parental occupational status, learning time, socioeconomic status, test effort, directed instruction, and disciplinary climate all stayed among the most stable predictors.

The authors are explicit about the limits of their contribution. They do not claim to have discovered new determinants of mathematics achievement; domains such as socioeconomic resources, home literacy environments, learning time, and classroom climate are already well established. The value lies in showing how these familiar predictors behave under a rigorous, country-specific, survey-weighted, plausible-value-aware explainable machine-learning framework, and in separating three questions that are often conflated: which models predict best, which predictors are stable across systems, and which are context-specific. They also caution that the analysis is predictive rather than causal, that the ten-system sample is not statistically representative of global education, that the 33-predictor pool cannot exhaust all relevant influences such as school policies or teacher characteristics, and that cross-national questionnaire comparisons may be affected by response styles, translation, and measurement comparability.

The implications reach well beyond this dataset. The framework, with its train-only feature selection, school-level holdout evaluation, replicate-weight variance estimation, and all-plausible-value pooling, offers a reproducible template that could be extended to more PISA systems, to other assessments such as TIMSS and PIRLS, to school-level and system-level predictors, and to fairness analyses examining whether prediction errors or SHAP patterns differ across gender, socioeconomic, immigrant, or language groups. Repeated-cycle analyses could test whether cross-national stability patterns persist as PISA evolves. For a field increasingly drawn to black-box prediction, the study makes a compelling case that explainability and methodological rigor are not optional extras but the foundation of responsible machine learning in comparative education.

Subject of Research: Explainable machine learning applied to predicting and interpreting mathematics achievement in PISA 2018 across ten education systems.

Article Title: Explainable AI for predicting and interpreting mathematics achievement: a cross-national analysis of PISA 2018

Article References: Liu, L., & Dai, R. (2026). Explainable AI for predicting and interpreting mathematics achievement: a cross-national analysis of PISA 2018. Large-scale Assessments in Education, 14(1), Article 46. https://doi.org/10.1186/s40536-026-00320-y

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00320-y

Keywords: PISA 2018, mathematics achievement, explainable artificial intelligence, SHAP, CatBoost, machine learning, survey weights, plausible values, large-scale assessment, socioeconomic status, comparative education, feature selection

The study, led by Yi-Hua Lin and Chien-Chih Chen of National Taipei University of Education together with Kuo-Sheng Chen of National Chengchi University and Jessica Lin of Newcastle University, deliberately avoids the most common yardstick in endowment research: the size of the fund. Instead, the authors apply policy instrument theory, a framework from political science that classifies the tools governments use to steer behavior, from compulsory mandates and legal authorization to financial incentives and voluntary, information-based nudges. Their question is configurational rather than causal: what combinations of policy instruments create the institutional conditions under which donated money can be legally separated from state budgets, accumulated over decades, professionally managed and independently audited?

Taiwan provides the study’s central puzzle. In 1999, the island introduced a university fund system intended to push public universities toward revenue diversification and financial self-responsibility, a reform widely seen as a step toward the autonomy enjoyed by universities in the United States or the United Kingdom. Yet more than two decades later, donation-based resources remain weakly institutionalized. Under Taiwan’s unified public finance framework, donations are recognized, but they are folded into the same budgeting, accounting and audit machinery that governs appropriated public funds. There is limited legal space for a gift to be set apart, invested for the long term, or managed with the strategic independence that endowment governance normally requires. The result, the authors find, is that even willing donors and willing universities operate inside a system that cannot easily hold and grow charitable capital.

The contrast with Singapore is instructive. The city-state combines compulsory, mixed and voluntary instruments into a coherent package. Legal authorization gives universities and their affiliated foundations distinct standing to receive and manage gifts; matching grant schemes multiply the value of private donations by coupling government money to philanthropic dollars; tax incentives reward donors; and audit mechanisms keep the resulting funds accountable. Singapore’s Ministry of Education has explicitly promoted alumni ties and community support for tertiary education, and institutions such as Nanyang Technological University maintain structured giving programs. The policy mix, the study argues, does not merely encourage donations, it constructs the legal and financial plumbing through which donations become durable endowment capital.

Hong Kong offers perhaps the most mature example of matching grants in the region. Through successive rounds of its Matching Grant Scheme administered under the University Grants Committee, the government has matched private donations to the eight publicly funded universities, converting philanthropic enthusiasm into enlarged endowment pools while embedding transparency and reporting requirements. A new round of a Research Matching Grant Scheme covering 2025 to 2029 extends the logic to research funding, as outlined in a 2024 proposal to Hong Kong’s Legislative Council. The scheme’s design illustrates what the authors call a mixed instrument: government does not compel giving, but it changes the payoff structure so that every private dollar works harder, and it simultaneously imposes governance conditions that shape how the money is held and spent.

China presents a third configuration. University foundations have proliferated across the mainland, giving institutions dedicated legal vehicles for receiving and managing donations, supported by tax-exemption rules for charitable organizations. Research on Chinese foundations documents both the promise and the friction of this model: tax-exempt status varies, and voluntary disclosure practices differ widely across foundations, shaping donor trust. The study notes that philanthropic culture in China is also inflected by what sociologist Fei Xiaotong famously described as a differential mode of association, in which giving flows preferentially along networks of kinship, locality and personal connection. Governance arrangements interact with this social texture, producing foundation-based endowment development that is expanding rapidly but unevenly.

Methodologically, the researchers coded legislative texts, policy documents and governance frameworks across the four jurisdictions, drawing on documentary and data sources assembled in comparative tables and an explicit analytical coding scheme. The authors are careful about what their design can and cannot claim. Because comparable longitudinal donation data are unavailable across the four systems, they frame their conclusions as institutional and configurational inference rather than causal proof that any single instrument increases donation volume. This evidence-bounded stance is a deliberate contribution to comparative higher education method, echoing broader debates about how qualitative comparative research can balance contextual sensitivity with comparability, and about policy assemblage and policy mobility in education studies.

The theoretical payoff lies in showing that endowment development is a governance outcome before it is a fundraising outcome. Drawing on the new institutionalism in political science, the authors treat rules, budgets and audit regimes as the substrate on which philanthropic markets either flourish or stall. Where Singapore, Hong Kong and China deploy configurations of legal authorization, matching grants, tax incentives, foundation governance and audit mechanisms, Taiwan’s single unified public finance framework provides recognition without separation. Donations exist, but they cannot easily be legally distinguished, accumulated across years, or strategically managed, which in turn weakens the incentives for universities to build professional advancement operations and for donors to commit large, long-horizon gifts.

From this comparison the study distills concrete policy implications for Taiwan, each framed cautiously. The authors suggest piloting legally distinguishable endowment vehicles that would allow donated funds to be separated from appropriated budgets without dismantling public financial oversight. They propose piloting matching grants, in the manner of Hong Kong’s schemes, while separately evaluating complementary tax incentives so that each instrument’s effects can be assessed on its own terms. They recommend selectively exploring differentiated financial governance, allowing leading universities more autonomy in managing donated assets under strengthened accountability. Finally, they emphasize transparency and communication, arguing that public trust in how donated money is held, invested and spent is itself a policy instrument that governments can sharpen.

The broader significance of the research extends beyond Taiwan. As governments worldwide push universities toward diversified revenue in an era of flat or declining public appropriations, the study warns that simply exhorting institutions to fundraise is unlikely to work if the underlying legal and financial architecture cannot accommodate endowments. Policy instrument configurations, the authors conclude, should be examined as institutional conditions for endowment-related governance, a lens that reframes university philanthropy not as a test of institutional charm but as a design problem in public finance. For East Asian higher education, where world-class ambitions increasingly collide with constrained state budgets, that reframing may prove one of the most consequential ideas in the debate over how universities pay for their futures.

Subject of Research: Comparative analysis of governance arrangements and policy instruments shaping university endowment development in Taiwan, Singapore, Hong Kong and China

Article Title: Governance arrangements and policy instruments in university endowment development: a comparative analysis of Taiwan, Singapore, Hong Kong, and China

Article References: Lin, Y.-H., Chen, C.-C., Chen, K.-S., & Lin, J. (2026). Governance arrangements and policy instruments in university endowment development: a comparative analysis of Taiwan, Singapore, Hong Kong, and China. Higher Education. https://doi.org/10.1007/s10734-026-01771-6

Image Credits: AI Generated

DOI: 10.1007/s10734-026-01771-6

Keywords: university endowments, higher education governance, policy instruments, Taiwan, Singapore, Hong Kong, China, matching grants, tax incentives, university finance, foundation governance, comparative education