Bibliographic record
Abstract
Abstract Following conflict and injustice, leaders of formerly antagonistic groups sometimes apologize for the harms that their groups perpetrated. For example, in 2002 the Irish Republican Army (IRA) apologized for slaying non–enemy combatants in their violent campaign for Irish independence. Government leaders occasionally apologize decades and even centuries after an injustice. Recently, for example, the Canadian and Australian governments apologized for abuse of Aboriginal populations in the nineteenth and twentieth centuries, seven American states apologized for their role in transatlantic slavery, and British prime minister Tony Blair apologized for the Irish Potato Famine. Some scholars argue that apologies, even belated ones, help heal the wounds of the past and promote reconciliation. Others oppose government apologies, arguing that they are ineffective and sometimes harmful. Politicians apologize for many kinds of transgressions, but no apologies generate the same degree of controversy as those for historical injustices. Below, we summarize the existing research on apologies, focusing on political apologies for historical injustices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".