Bibliographic record
Abstract
Abstract The practice of official apology has a fairly poor reputation. Dismissed as ‘crocodile tears’ or cheap grace, such apologies are often seen by the public as an easy alternative to more punitive or expensive ways of taking real responsibility. I focus on what I call the role‐playing criticism: the argument that someone who offers an apology in public cannot be appropriately apologetic precisely because they are only playing a role. I offer a qualified defence of official apologies against this objection, considering them through the lens of fiduciary duties. This focus draws our attention to formal or impersonal relationships that are nevertheless normatively rich, capable of sustaining trust, concern, and care. At the same time, I highlight several pitfalls for fiduciary apologisers, including the tension between apology as a mode of truth telling and the duty of confidentiality. I consider whether the fiduciary apologiser, in reflecting on her fiduciary obligations, has ‘one thought too many’ for genuine apology, and argue that the issue of mixed motives is not limited to fiduciary contexts, cautioning against excessive idealism in our conception of apology. I conclude with some reflections on possible conflicts between fiduciary obligations and the conscientious desire to apologise.
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 | 0.016 | 0.029 |
| 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 | 0.056 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".