Social inequality in shadow education: The role of high-stakes testing
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
Against the background of the worldwide expansion of shadow education, research shows that students from high socio-economic status (SES) backgrounds participate more in shadow education than students from disadvantaged SES backgrounds. We relate these social inequalities in shadow education participation to institutional features of educational systems. More specifically, we argue that the effect of socio-economic background on participation in shadow education will be stronger in countries characterized by high-stakes testing. Using data from the Programme for International Student Assessment for the year 2012 (PISA 2012), we show that higher SES students participate more in shadow education. For three out of four indicators of shadow education, this relationship is stronger in countries that are characterized by high-stakes testing but only when accounting for unobserved country differences.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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