Bibliographic record
Abstract
It is disappointing, therefore, that theoretical work on the topic has largely failed to take seriously questions of gender. 2 The severity of this omission is especially striking once we acknowledge the growing body of evidence concerning "the ways gender plays a role in generating, or at least shaping, the forms and the effects of political violence perpetrated under authoritarian regimes and during armed conflict."3 Gender is deeply implicated in some of the most serious harms for which public apology is invoked as remedy.Similarly, feminist work in philosophy, psychotherapy, and on restorative justice has highlighted the significant role that gender plays in practices of apologizing; indeed, this research suggests that acts of apology (both private and public, political and apolitical) are already gendered, often in problematic ways.4 It would seem that gender rears its head when we consider the figure of the apologizer, 2 Aaron Lazare and Nick Smith both include short sections on gender and apology, but in both cases their discussion is largely limited to the comparative apology styles of men and women and, in particular, claims based on sociologist Deborah Tannen's work, that women apologize more than men.My intention in this paper is to convince the reader that the relationship between gender and the practice of apologizing goes far deeper than the quality and quantity of men's and women's individual utterances.See Aaron Lazare,
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".