Why is Adoption Like a First Nations’ Feast?: Lax Kw’alaam Indigenizing Adoptions in Child Welfare
Bibliographic record
Abstract
Have you ever wondered about how to be culturally-sensitive in adoption approaches with Aboriginal people? Have you wanted ideas on how to more effectively engage First Nations adoptive-parents? Did you consider how leadership for social workers could assist in adoption outcomes for Aboriginal children? This article chronicles a study of the adoption experiences of the members of a First Nations community in Northwestern British Columbia, Canada. The results indicated that despite an overwhelmingly negative history with the adoptions and child protection system, many First Nations people are not only open to adoption but perceive it as an integral part of their traditional parenting practices. There is an overarching desire to have children who have been previously adopted outside the community returned to their hereditary lands. A series of recommendations for a more culturally-sensitive adoption practice were identified including: 1) improved information, 2) on-going community-government consultation, 3) cultural preservation, 4) social work training, and 5) government policy changes. The article will encourage curiosity regarding social work leadership and how this framework can be instrumental when working with Aboriginal culture. The implications of the study for the role of social workers as leaders in the creation of a new, culturally-sensitive adoption practice are discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".