Epistemic Diversity and Epistemic Advantage: A Comparison of Two Causal Theories in Feminist Epistemology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Feminist epistemology aims to propose epistemic reasons for increasing the representation of women or socially subordinated people in science. This is typically done—albeit often only implicitly—by positing a causal mechanism through which the representation of sociodemographic minorities exerts a positive effect on scientific advancement. Two types of causal theories can be identified. The “epistemic diversity thesis” presents a causal path from sociodemographic diversity to scientific progress mediated by epistemic diversity. The “thesis of epistemic advantage” proposes a causal path from social subjugation to capacity for inquiry. The latter theory is defined with substantial ambiguity in the existing literature, and I present an explicit causal reformulation that disambiguates it. The epistemic diversity thesis focuses on the effect of group composition on collective epistemic performance and is largely silent about what kind of characteristics lead to individual epistemic excellence. On the other hand, the thesis of epistemic advantage seeks to identify sociodemographic background conditions that make certain epistemic agents strictly better knowers or inquirers than others and pays little attention to the synergistic effects of diverse group composition. Such a difference in the causal structure reflects the diverging political characteristics of the two theories.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it