Bibliographic record
Abstract
Trigger warnings have been the flashpoints of many discussions in recent years. A prominent claim among those arguing against trigger warnings is what I will call the “coddling argument” (CA), according to which trigger warnings coddle by allowing people to avoid ideas that they disagree with or find difficult. In this paper, I try to both make sense of and refute the coddling argument from a vice epistemological perspective. As I argue, CA is best understood as an expression of concern about the encouragement of epistemic vices, specifically in higher education, which lead to people avoiding and closing themselves off from difficult or challenging topics. I argue that this is misguided: trigger warnings exist for people who need to be warned about certain contents because they already know about these issues. Demands for such warnings are usually made by those who have themselves experienced the difficult things defenders of CA purport they are trying to hide from. We do, however, need to take into account that trigger warnings might be misused by those who really do need to learn about topics that might be a trigger for others, and I will discuss how this issue could be addressed.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".