Different Voices, Perfect Storms, and Asking Grandma What She Thinks: Situating Experimental Philosophy in Relation to Feminist Philosophy
Bibliographic record
Abstract
At first glance it might appear that experimental philosophers and feminist philosophers would make good allies. Nonetheless, experimental philosophy has received criticism from feminist fronts, both for its methodology and for some of its guiding assumptions. Adding to this critical literature, I raise questions concerning the ways in which “differences” in intuitions are employed in experimental philosophy. Specifically, I distinguish between two ways in which differences in intuitions might play a role in philosophical practice, one which puts an end to philosophical conversation and the other which provides impetus for beginning one. Insofar as experimental philosophers are engaged in deploying “differences” in intuitions in the former rather than the latter sense, I argue that their approach is antithetical to feminist projects. Moreover, this is even (and perhaps especially) the case when experimental philosophers deploy “differences” in intuitions along lines of gender.
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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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.090 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".