To Apologize or Not to Apologize: National Histories and Official Apologies
Bibliographic record
Abstract
Canadian political scientist Alan Cairns has described the history recounted in Canada's 1996 Royal Commission of Aboriginal People's Report as “a historical storehouse of mistreatment, deception, arrogance, dispossession, coercion, and abuses of power.” Many scholars, politicians, policy makers, and segments of the public in Australia, New Zealand, and the United States have reached similar conclusions about their national histories and treatment of indigenous people and other minority groups. Government apologies have followed in the wake of official disclosures in certain of our cases, but not all of them. What drives government to apologize or refuse to apologize and what drives groups to demand them? Why are apologies desirable? This chapter examines state motivations for granting apologies and group motivations for demanding them. It contends that state officials will apologize when they ideologically support and seek to advance minority rights. This is not to suggest, however, that state officials will necessarily initiate the apologies, but rather that they are more likely to apologize when pressured. Aggrieved groups, on the other hand, insist and/or receive apologies, perceiving them as legitimatizing both their claims of historical mistreatment and present-day demands for rectification and self-determination. As state and group actors perceive them, apologies' efficacy in sustaining minority demands derives, in part, from their essential qualities. Apologies perform three tasks, with national histories and their reinterpretations necessarily at their center. First, apologies validate reinterpretations of history by formally acknowledging past actions and judging them unjust.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.032 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".