The Intergenerational Effect of Forcible Assimilation Policy on School Performance
Bibliographic record
Abstract
For nearly a century, the Canadian government forcibly separated indigenous children from their families and placed them in live-in institutions, known as Indian Residential Schools. Close to 50 percent of North American Indian children have a family member who attended residential school in Canada, and many speculate that the legacy of residential schooling has contributed to the educational struggles indigenous children face today. Using a unique confidential data set, I identify the effects of mothers ' attending a residential school on their children. I find that children whose mother attended a residential school are less likely to perform well in school, less likely to enjoy school or to get along with their teachers, but fare better along health dimensions and receive no less parental investment. I provide evidence that these findings are not due to the location choice of the parents and argue that these findings are consistent with a standard Heckman model of skill production where parental attitudes toward education play a pivotal role. I add to the existing literature on childhood development by demonstrating that policies that negatively influence parental attitudes toward education may negatively influence the next generation even if the policy has little or positive effects on parental skills, investment and child health.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".