These Are Not Just Words: A Cross-National Comparative Study of the Content of Political Apologies
Bibliographic record
Abstract
Countries around the world increasingly try to redress past human rights violations by offering apologies. The debates surrounding many of these political apologies suggest they do not necessarily satisfy victims’ needs. Little is known, however, about the actual content of these apologies and the extent to which they include the elements that are often seen as essential to healing processes. In this exploratory study, we conducted a cross-national comparative analysis of the texts of political apologies (<em>N</em> = 203, offered by 50 countries) and coded whether they included a statement of sorry, apology, or regret (IFID), and an acknowledgement of wrongdoing, acceptance of responsibility, promise of non-repetition, promise of reparations, recognition of victim suffering, victim re-inclusion, victim praise, or a recognition of moral values/norms. We found that the majority of political apologies only include a selection of these elements, with some countries offering more comprehensive apologies than others. Most apologies, however, do contain an IFID, an acknowledgment of wrongdoing and a recognition of suffering, although there is variation in how this is expressed. This variation can be linked to the receiving group (i.e., within-country or not), the contentiousness of the apology in a country and – albeit weakly – the cultural context. Based on these findings, we suggest that when considering the impact of political apologies, it is crucial to consider quantity (how many apology components are included) as well as quality (how this is done).
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".