Bibliographic record
Abstract
Indigenous peoples have been reclaiming jurisdiction over their child welfare services and Western society has been increasingly acknowledging that Indigenous peoples are in the best position to provide these services. While the number of Indigenous social workers has historically been low, especially when compared to the population they serve, their numbers seem to be on the rise. In spite of that reality, most social service organizations continue to operate from a Western perspective, with little attention paid to the ways in which they must change in order to provide space for the Indigenous social workers they employ. This study explores the experiences of nine First Nations and Métis social workers in British Columbia (BC). The researcher, a Métis scholar and former child welfare social worker, conducted data collection and analysis through a Métissage framework, using semi-structured interviews. Thematic analysis revealed nine themes, including the need for (1) Knowledgeable leadership that supports autonomy; (2) Flexibility in practice; (3) Policy that fits both Indigenous and Western paradigms; (4) Relationships with other supportive social workers; (5) Support to navigate overlap between the personal and the professional; (6) Set standards/experienced co-workers; (7) Equitable workplace resources; (8) Respect regarding Indigenous identity, and; (9) Supports to maintain wellness. Recommendations suggest how this information can be used by organizations to better support the Indigenous social workers they employ.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".