Bibliographic record
Abstract
A student’s peers are often thought to influence his or her educational outcomes. If so, an unequal distribution of advantaged and disadvantaged students across schools (“sorting”) in a community will amplify existing inequalities. This paper explores the relationship between the degree of sorting across schools within a community and educational inequality as measured by the variance of standardized high school exam scores within the community. Cross-sectional OLS estimates suggest that the vari-ance of test scores is related to sorting by ethnicity, but not to sorting by income or parental education. We then implement two strategies for addressing endogeneity in the degree of sorting: a standard un-observed effects (first-difference) approach, and a first-difference/instrumental variables approach in which the structure of school choice (number and relative size of schools) is used to construct instru-ments for the degree of sorting. The results from both approaches indicate that the variance of test scores is related to sorting by home language and parental education, but not to sorting by income. Our results also suggest that reducing sorting would have little effect on inequality of outcomes in the typical Alberta community, but would have substantial effects in the larger cities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".