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Record W4403106057 · doi:10.18357/kula.294

Epistemic Injustice or Epistemic Oppression?

2024· article· en· W4403106057 on OpenAlexaffvenue
Amandine Catala

Bibliographic record

VenueKULA knowledge creation dissemination and preservation studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsOppressionEpistemologyInjusticeSociologyPhilosophyPsychologyPolitical scienceSocial psychologyPoliticsLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.509
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations6
Published2024
Admission routes2
Has abstractyes

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