Bibliographic record
Abstract
“Mansplaining” is by now part of the common cultural vernacular. Yet, academic analyses of it—specifically, philosophical ones—are missing. This paper sets out to address just that problem. Analyzed through a lens of epistemic injustice, the focus of the analysis concerns both what it is, and what its harms are. I argue it is a form of epistemic injustice distinct from testimonial injustice wherein there is a dysfunctional subversion of the epistemic roles of hearer and speaker in a testimonial exchange. As these are roles of power and are crucial to our existence and functioning within epistemic communities, the wrong and harms suffered from this injustice are serious and, I argue, distinct from other types already discussed in the literature. I close by considering an alternative model of mansplaining as a form of silencing, as well as briefly diagnosing its general underlying cause and possible solutions.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".