Inequality of Opportunity for Education : The Case of Turkey
Bibliographic record
Abstract
This paper seeks to measure inequality \n of opportunity for education in turkey, taking into account \n both the quantity (attainment) and the quality of schooling \n (achievement). Using DHS data, large gaps in age-enrollment \n profiles are documented across genders, regions, and family \n backgrounds. The gender gap is particularly pronounced in \n the Easter provinces, in rural areas, and for poorer and \n larger households. PISA data show that morally irrelevant \n circumstances also affect achievement. The lower bound for \n the share of the variance in test scores that is accounted \n for by such circumstances in between a quarter and a third, \n depending on the subject and on the procedure adopted to \n correct for sample selection. Among those circumstances, \n family background variables such as parental education, \n father's occupation and the ownership of books, \n cultural possessions and electronics, seem to account for \n the largest inequality shares. Once the composition of \n families is controlled for, spatial location is considerably \n less important.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".