Bibliographic record
Abstract
point the way toward meaningful advances in mental health research pertaining to Indigenous peoples, illuminating the distinctive problems and predicaments that confront these communities as well as unrecognized or neglected sources of well-being and resilience. As we observe in this introductory essay, future research will benefit from ethical awareness, conceptual clarity, and methodological refinement. Such efforts will enable additional insight into that which is common to Indigenous mental health across settler societies, and that which is specific to local histories, cultures and contexts. Research of this kind can contribute to nuanced understandings of developmental pathways, intergenerational effects, and community resilience, and inform policy and practice to better meet the needs of Indigenous individuals, communities and populations.
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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 | 0.047 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".