Institutional contestations and educational equity: incorporation of the marginalized in National Education Policies Worldwide, 1960–2019
Bibliographic record
Abstract
Abstract The notion that everyone deserves education and that governments need to strive for this ideal has been legitimized and expanded worldwide over the past few decades, as a central part of the successful establishment of a liberal world order. However, the recent resurgence of global illiberalism poses a threat to liberal ideas of equity and diversity, potentially impeding progress toward a more inclusive education system. Against this backdrop, I investigate the extent to which countries around the world introduced policies to incorporate historically disadvantaged populations into education between 1960 and 2019 and what sociocultural factors are associated with the adoption of education policies for the marginalized. Using event count analyses and a novel longitudinal dataset on global education reforms, I show that countries are more likely to adopt education policies for the marginalized when liberalism is globally prevalent, while they are less likely to do so when illiberalism is globally prominent. I also find that countries’ linkages to international liberal or illiberal institutions, including through organized transnational networks, as well as domestic sociopolitical environments, relate to the adoption of education policies for the marginalized. These results help illuminate how countries exist in a world filled with opposing cultural models and through which mechanisms their approaches to educational equity are shaped by such global forces. While focused on education for the marginalized, these findings offer insights into understanding inequality in a changing world context of growing illiberalism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".