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Record W4226485645 · doi:10.5206/fpq/2021.4.13713

Racial Injustice and Information Flow

2021· article· en· W4226485645 on OpenAlexvenueno aff
Eric Bayruns García

Bibliographic record

VenueFeminist Philosophy Quarterly · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsInjusticeFlow (mathematics)SociologyPolitical sciencePsychologyMechanicsSocial psychologyPhysics

Abstract

fetched live from OpenAlex

I submit that the critical epistemology of race and standpoint literature has not explicitly focused on the properties of information about racial or gender injustice in a way similar to how epistemologists have focused on propositions and information when they describe propositional justification. I present an account of information flow in which I describe information in the racial-injustice-information domain in a way similar to how epistemologists describe propositional justification. To this end, I argue (C1) that if subjects in racially unjust societies tend to violate norms that promote a community’s reliable information flow because racial prejudice is widely held in racially unjust societies, then racial injustice can make information flow less reliably in a community. And I argue (C2) that if racial prejudice can make information flow less reliably in a community, then information that nondominant subjects are more likely to have will less reliably flow to community members who lack it.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations2
Published2021
Admission routes1
Has abstractyes

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