Inequalities in Test Scores between Indigenous and Non-Indigenous Youth in Canada
Bibliographic record
Abstract
This paper documents a robust achievement gap between the math scores of Indigenous and non-Indigenous youth in Canada between 1996 and 2008. Using data from the restricted-access National Longitudinal Survey of Children and Youth we show that after controlling for a rich set of observables, students who self-identify as Indigenous perform 0.31 standard deviations lower on a standardized math test compared to their non-Indigenous counterparts. We find that this test gap emerges by the age of 12, and it did not decline between 1996 and 2008, despite the recommendations of the 1996 Royal Commission on Aboriginal Peoples to ameliorate the public education system for Indigenous students. Counterfactual estimates from the decomposition method of Lemieux (2002) suggest that the test gap among the lowest performing students would have been eliminated if Indigenous students faced the same level of and returns to observable characteristics as non-Indigenous students. This exercise does not result in a narrowing of the test gap in the upper tail, suggesting that unobservables, rather than observables, are driving the majority of the test gap among high achieving students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".