Bibliographic record
Abstract
This paper proposes that, in many cases, conversational norms permit gaslighting when socially subordinate speakers report systemic injustice. Section 1 introduces gaslighting and the kinds of cases on which I focus—namely, cases in which multiple people gaslight. I give examples and statistics to suggest that these cases are common in response to reports of race- or gender-based injustice; and I appeal to scholarship on epistemologies of ignorance to suggest that this kind of gaslighting is common because it is systematically produced by dominant epistemic systems. Section 2 draws on Lynne Tirrell’s account of language games that’ve been influenced by oppression to make the case that conversational norms make gaslighting “appropriate” when socially subordinate speakers report systemic injustice. Together, these points make the case that the kind of gaslighting discussed in this paper (i) occurs systematically and (ii) is mutually reinforcing with systems of ignorance. The discussion is meant to help us understand and address the conditions that make gaslighting commonplace. If it’s true that gaslighting occurs systematically in part thanks to our warped conversational norms, then we may be able to mitigate the prevalence of gaslighting by attending to these norms.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".