Issue 1: Reimagining Overrepresentation Research: Critical Reflections on Researching the Overrepresentation of First Nations Children in the Child Welfare System
Bibliographic record
Abstract
This paper builds on the experiences of the first author in doing research on the overrepresentation of First Nations children in child welfare systems in Canada. Six lessons are presented: (1) overrepresentation is an inherently quantitative construct; (2) overrepresentation is an inherently comparative construct; (3) a focus on overrepresentation draws attention to the needs of specific groups, but may obscure the need for broader systemic reform; (4) available data relies on, but incompletely represents, decision-maker perspectives; (5) available data emphasizes point-in-time decisions; and (6) ambiguity in data must be very clearly acknowledged. Building on discussion of these lessons, we explore implications for future research directions and highlight considerations for child welfare policy and practice.
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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 | 0.261 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.046 | 0.162 |
| Scholarly communication | 0.038 | 0.037 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.021 | 0.037 |
| 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".