Issue 2: How We Can “Bell the Cat”: African Canadian Perspectives of the Canadian Child Welfare System
Bibliographic record
Abstract
I am very grateful to Canada for what it has done for impoverished people, refugees, and immigrants from all over the world; as a Christian refugee who has benefited from this country’s generosity, I feel an obligation to give back in my own special way. It is my hope to describe a refugee parent’s perspective of Canada’s child welfare system. Throughout the world, Canada is held in high esteem for its social services and respect for cultural differences, often making it an ideal refuge. Many newcomers have experienced suffering and deprivation before immigrating to Canada, and many believe they can live their dreams when they arrive. However, their dreams are often not given an opportunity to become reality because of systemic racism, which I understand to be a form of intentional marginalization. This injustice is especially prominent in the child welfare system, which faces criticism from different advocacy groups due to the overrepresentation of Aboriginal and African Canadian children in state care.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.085 | 0.030 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 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".