Bibliographic record
Abstract
ABSTRACT Apologies often create expectations of meaningful change and repair. Yet when institutions or states deliver apologies for past wrongs that lack substantive reparative action, they risk deepening, rather than redressing, the harms they acknowledge. In this article, I examine what I call ‘botched apologies’ that can be performative, temporally disconnected from the ongoing effects of harm, and ultimately serve the interests of perpetrators. I argue that these botched apologies inflict distinct epistemic harms: they gaslight the victims, silence them, appropriate their hermeneutical resources, and exploit them. Using an epistemic reparations framework, I propose four non‐exhaustive conditions for epistemically responsible apology: truthfulness, testimonial uptake, hermeneutical openness, and reciprocal epistemic labour.
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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".