Expectations Among Aboriginal Peoples in <scp>C</scp>anada Regarding the Potential Impacts of a Government Apology
Bibliographic record
Abstract
After continued pressure, the Canadian government offered an apology to Aboriginal peoples for its role in the Indian Residential School (IRS) system, where children were removed from their families in an effort to assimilate the Aboriginal population. Although the apology was sought after, it was unclear what Aboriginal peoples expected it to accomplish in relation to their treatment and quality of life within Canada. Quantitative and qualitative analyses revealed that, although Aboriginal adults (N = 164) felt the apology could potentially be a first step towards improved relations with the government and non‐Aboriginal Canadians, expectations that such changes would actually come to fruition were generally pessimistic. In exploring predictors of such expectations, path analysis indicated that those who had been intimately impacted by IRSs reported greater perceived discrimination that, in turn, was associated with lowered intergroup trust and forgiveness. Those who perceived high levels of discrimination were less likely to expect changes following the apology, which was mediated by the low levels of intergroup trust and forgiveness towards the government, but not towards non‐Aboriginal Canadians. Essentially, an apology was not enough to elicit hope for improved intergroup relations, especially when perceptions of continued discrimination impeded the restoration of intergroup trust and forgiveness.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".