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What is Epistemological Violence in the Empirical Social Sciences?

2010· article· en· W2149697221 on OpenAlexaff
Thomas Teo

Bibliographic record

VenueSocial and Personality Psychology Compass · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsYork University
Fundersnot available
KeywordsInterpretation (philosophy)EpistemologyObject (grammar)Action (physics)Subject (documents)Context (archaeology)Meaning (existential)PsychologyEmpirical researchSocial psychologySociologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

Abstract This article discusses the meaning of epistemological violence in the empirical social sciences. It is argued that the concept is closer to personal than to structural violence in that it has a subject, an object, and an action, even if the violence is indirect and nonphysical: the subject of violence is the researcher, the object is the Other , and the action is the interpretation of data that is presented as knowledge. Using a hypothetical example, the problem of interpretation in empirical research on the Other is discussed. Epistemological violence refers to the interpretation of social‐scientific data on the Other and is produced when empirical data are interpreted as showing the inferiority of or problematizes the Other , even when data allow for equally viable alternative interpretations. Interpretations of inferiority or problematizations are understood as actions that have a negative impact on the Other . Because the interpretations of data emerge from an academic context and thus are presented as knowledge, they are defined as epistemologically violent actions. Problems, consequences, and practices surrounding this concept are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.117
metaresearch head score (Gemma)0.219
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1170.219
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0110.010
Science and technology studies0.0150.225
Scholarly communication0.0260.037
Open science0.0030.018
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0030.000

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 head0.317
GPT teacher head0.492
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
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

Citations321
Published2010
Admission routes1
Has abstractyes

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