Strategies to Revive Traditional Decision-Making in the Context of Child Protection in Northern British Columbia
Bibliographic record
Abstract
For indigenous peoples, recovering from colonial rule and aspiring to flourish, the revival of traditional decision making (TDM) is considered essential. However, transitioning from established colonial practices to TDMs is not well understood. In this paper we identify some of the challenges experienced by a First Nation urban community in the north east of British Columbia as they have tried to develop and implement a culturally-relevant child and family-centered traditional decision-making (TDM) process in the context of government-regulated child protection system. Specifically, we problematize a collaborative decision-making strategy—Family Group Conferencing (FGC). FGCs are premised on values of collaboration, participation, and empowerment, and because this strategy shares many of the values and aspirations of Traditional Decision-Making (TDM), there is a temptation to directly download and incorporate FGCs into the TDM model. In this paper we explore five challenges that warrant particular attention in developing TDM model in this contemporary context: 1) power, 2) cultural adaptability, 3) family support and prevention, 4) coordinator “neutrality”, and 5) sustainable support. We conclude with eight recommendations to overcome these challenges while developing TDMs in a child protection context.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".