Bibliographic record
Abstract
Abstract Jean-Marc Coicaud's article begins by stressing the contemporary importance and the current trend of political apology. Recent political apologies offered in Australia and Canada to their indigenous populations form a significant part of this story. He then analyzes a number of intriguing paradoxes at the core of the dynamics of apology. These paradoxes give meaning to apology but also make the very idea of apology extremely challenging. They have to do with the relationships of apology with time, law and the unforgivable. The most intriguing of these paradoxes concerns apology and the unforgivable. Indeed, the greater the wrong, the more valuable the apology. But, then, the more difficult it becomes to issue and to accept an apology. This latter paradox is namely examined in the context of mass crimes, taken from Europe, Africa and Asia. As a whole these paradoxes are all the more intriguing considering what apology in a political context aims to accomplish, for the actor who issues the apology, for the one who receives it, for their relationship, and for the social environment in which this takes place. Jean-Marc Coicaud concludes his article by outlining what the rise of apology means for contemporary political culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".