Worldwide shadow education and social inequality: Explaining differences in the socioeconomic gap in access to shadow education across 63 societies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines the cross-national differences in socioeconomic accessibility to shadow education (SE) across 63 societies. Drawing on arguments from two competing theoretical models either emphasizing cross-national cultural, economic, and institutional differences (e.g. model of secondary schooling, scale of SE) or universally working social reproduction mechanisms (e.g. enrichment features of SE), this study provides a novel approach to understanding the role of SE for social inequality. More specifically, while the first model explicitly allows equality in access to SE, the latter suggests that SE fosters inequality under all circumstances. Using data from the 2012 Program for International Student Assessment (PISA) and official sources, first, the difference in the probability of top in comparison to bottom socioeconomic strata to use SE is predicted separately for all societies, before analyzing what causes the found considerable cross-national variation in the socioeconomic gap in access to SE at the country level. Results indicate that differences in SE access are linked to incentives for high-performing students to use SE. These incentives are especially common in societies with higher educational institutional differentiation (e.g. early or mixed tracking schooling models). In societies with less stratified education systems, access to SE is more equal, wherefore the potential effect of SE to social inequality is dampened. Overall, findings suggest that simple generalizations based on existing theoretical models provide no comprehensive explanation for the connection between SE and inequality. Instead, prominent beliefs about the relationship between SE and inequality are questioned.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it