Bibliographic record
Abstract
Intense interest in past injustice lies at the centre of contemporary world politics. Most scholarly and public attention has focused on truth commissions, trials, lustration, and other related decisions, following political transitions. This book examines the political uses of official apologies in Australia, Canada, New Zealand, and the United States. It explores why minority groups demand such apologies and why governments do or do not offer them. Nobles argues that apologies can help to alter the terms and meanings of national membership. Minority groups demand apologies in order to focus attention on historical injustices. Similarly, state actors support apologies for ideological and moral reasons, driven by their support of group rights, responsiveness to group demands, and belief that acknowledgment is due. Apologies, as employed by political actors, play an important, if underappreciated, role in bringing certain views about history and moral obligation to bear in public life.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".