Bibliographic record
Abstract
This chapter canvasses more recent developments in Canada post-apology. Like the previous chapter on Australia, it recognises the importance of Indigenous peoples to guide government in maintaining the apology’s promise of ‘never again’. This chapter assesses more recent developments through this lens. This includes approaches to providing redress to Residential School Survivors, fulfilling the Calls to Action of the Truth and Reconciliation Commission of Canada (‘TRC’) with respect to ending the violence against Indigenous women and girls and implementing the United Nations Declaration on the Rights of Indigenous Peoples , and addressing the shortcomings of the child welfare system. Since the Trudeau government’s election, it has committed to implement all the TRC’s recommendations and has supported the findings of subsequent inquiries, most notably, the finding of genocide made by the National Inquiry into Murdered and Missing Indigenous Women and Girls. These developments reveal how Indigenous perspectives can change the national narrative to catalyse even more change. Despite this, poor implementation of the Indian Residential Schools Settlement Agreement , and slow progress and often resistance to Indigenous claims has brought the government’s commitment into question. These developments reveal how the government by ostensibly implementing change is maintaining the status quo in continuing to perpetrate injustices against Indigenous peoples.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.135 | 0.025 |
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".