Bibliographic record
Abstract
The concepts of epistemic injustice and epistemic oppression both aim to track obstacles to epistemic agencyーi.e., forms of epistemic exclusionーthat are undue and persistent. Indeed, the two terms are often used interchangeably. In this paper, I begin by addressing the question of whether the concepts of epistemic injustice and of epistemic oppression are in fact synonymous, and how we might articulate the relation between the two. I argue that while they partly overlap, the two concepts are not synonymous, and that one fruitful way to characterize their relation is by drawing on the distinction between systematic and incidental epistemic injustice. I then turn to the question of what might be gained or lost by focusing on one concept rather than the other. I argue that the concept of epistemic injustice, specifically that of incidental epistemic injustice, allows us to track certain types of undue and persistent obstacles to epistemic agency that the concept of epistemic oppression does not, but that should nonetheless be of interest to theorists of epistemic oppression. I close with some suggestions for further avenues to explore in order to gain a richer and more precise understanding of the various forms that problematic epistemic exclusion can take and how we might characterize them within our philosophical taxonomies.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.091 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".