Closing chapters of the past? Rhetorical strategies in political apologies for human rights violations across the world
Bibliographic record
Abstract
Over the past decades, an increasing number of countries have apologized for human rights violations in the recent or distant past. Although this has led to considerable debate about the value and meaning of apologies and their potential as a transformative mechanism, little is known about how countries across the world try to address and redress past wrongdoings in these statements. Relying on a database of apologies that have been offered worldwide by states or state representatives for human rights violations, we identified various rhetorical strategies that diverse countries use—to varying degrees—to (1) break from or acknowledge past wrongdoings, (2) bridge past wrongdoings with future intentions, and (3) bond with the intended recipients of the apology. In this article, we shed light on the strategies we identified in this regard. In doing so, we show how countries and their representatives use apologies not only or necessarily to address the needs of victims or their relatives, but also to portray and understand themselves, whereby there is substantial overlap in the types of rhetorical strategies and scripts that they use to accomplish this.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".