Promising practice for maintaining identities in First Nation adoption
Bibliographic record
Abstract
The purpose of this article is to explore the importance of identity in First Nation adoption. It is adapted from a PhD study completed by the author in 2005. The objectives of this study were: (1) describe how connectedness relates to health for First Nation adoptees, and (2) explore legislative, policy and program implications in the adoption of First Nation children. The findings suggest that, for First Nation adoptees, there is a causal relationship between connection to birth family, community and ancestral knowledge, adoption and health. The major finding is that loss of identity may contribute to impaired physical, spiritual, mental and emotional health for First Nation adoptees. This article provides suggestions on how identity can be preserved in First Nation adoption through programs, policies 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 | 0.056 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".