Bibliographic record
Abstract
What form must a theory of epistemic injustice take in order to successfully illuminate the epistemic dimensions of struggles that are primarily political? How can such struggles be understood as involving collective struggles for epistemic recognition and self-determination that seek to improve practices of knowledge production and make lives more liveable? In this paper, I argue that currently dominant, Fricker-inspired approaches to theorizing epistemic wrongs and remedies make it difficult, if not impossible, to understand the epistemic dimensions of historic and ongoing political struggles. Recent work in the theory of recognition—particularly the work of critical, feminist, and decolonial theorists—can help to identify and correct the shortcomings of these approaches. I offer a critical appraisal of recent conversation concerning epistemic injustice, focusing on three characteristics of Frickerian frameworks that obscure the epistemic dimensions of political struggles. I propose that a theory of epistemic injustice can better illuminate the epistemic dimensions of such struggles by acknowledging and centering the agency of victims in abusive epistemic relations, by conceptualizing the harms and wrongs of epistemic injustice relationally, and by explaining epistemic injustice as rooted in the oppressive and dysfunctional epistemic norms undergirding actual communities and institutions.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.103 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.010 |
| 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".