Bibliographic record
Abstract
Gaslighters make their targets feel defective for possessing mental states to which they are entitled. This kind of deceptive manipulation is universally condemned as wrongful and destructive by philosophers and psychologists, as it destroys its target’s epistemic agency and psychological well-being and can be epistemically unjust. But these issues apply to gaslighting that is downward-facing—where the powerful gaslight the vulnerable—and gaslighting is not always like this. When the traditional power dynamic is reversed and a vulnerable agent gaslights her powerful oppressor to defend herself from being wronged, I call it gaslighting-up and argue that such behavior is morally permissible and epistemically valuable. It enables abuse victims to avoid harm and to resist the very epistemic domination that is caused by abusive gaslighting, enables marginalized agents to safely protest bigotry and avoid the kind of testimonial injustice that they would face from traditional epistemic gaslighting, and even epistemically benefits its dogmatic targets by giving them a healthy dose of epistemic humility. So, rather than condemning gaslighting as intrinsically, universally bad, we should take a nuanced view and consider it in the context of real-world power dynamics and the role it plays in reinforcing and destabilizing these.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".