Reimagining the Potential of Feminist Epistemologies in Science
Bibliographic record
Abstract
Standpoint and feminist epistemologies have provided a number of theoretical advancements concerning the ways we ought to think about the production of knowledge across scientific disciplines. Despite these theoretical contributions, in this paper, we critique what we call the “thin” application/implementation of diversity within the epistemic practices of science and academia. As an alternative, we place these theories in conversation with recent philosophy of science and Indigenous epistemology focused on the epistemic aims of communal explanation and understanding. We contend that conceptions of diversity that focus on the standpoints of individual researchers and attempts to merely “add diversity and stir” make it more difficult for these epistemic goals of science to be achieved. We then argue that for diversity to contribute to increasing the variety of explanations and promote more substantive understanding of epistemic communities requires a “thick” implementation that incorporates lessons from standpoint, feminist, and Indigenous epistemologies into the heart of scientific practices.
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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 | 0.046 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.124 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".