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Record W2385211838 · doi:10.1163/1569206x-12341453

Elements of a Historical-Materialist Theory of Racism

2016· article· en· W2385211838 on OpenAlexaff
David Camfield

Bibliographic record

VenueHistorical Materialism · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicNames, Identity, and Discrimination Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRacismOppressionSociologyMaterialismIdeologyPrivilege (computing)Gender studiesPsychometrics of racismAnti-racismEpistemologyPolitical sciencePoliticsPhilosophyLaw

Abstract

fetched live from OpenAlex

This article aims to advance the historical-materialist understanding of racism by addressing some central theoretical questions. It argues that racism should be understood as a social relation of oppression rather than as solely or primarily an ideology, and suggests that a historical-materialist concept of race is necessary in order to capture features of societies shaped by historically specific racisms. A carefully conceived concept of privilege is also required if we are to grasp the contradictory ways in which members of dominant racial groups are affected by social relations of racial oppression. The persistence of racism today should be explained as a consequence of two dimensions of the capitalist mode of production – imperialism and the contribution of racism to profitability – and of a social property emergent from racism: the efforts of members of dominant groups to preserve their advantages relative to the racially oppressed.

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.006
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0060.043
Scholarly communication0.0060.007
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.056
GPT teacher head0.329
Teacher spread0.273 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations41
Published2016
Admission routes1
Has abstractyes

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