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Record W4321496715 · doi:10.1177/08969205231152560

Capitalism, Class Struggle and/in Academia

2023· article· en· W4321496715 on OpenAlexaff
Raju J Das

Bibliographic record

VenueCritical Sociology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsYork University
Fundersnot available
KeywordsCapitalismIdeologySociologyClass (philosophy)Class conflictReproductionWorking classPolitical economyWagePolitical scienceLawEpistemologyPolitics

Abstract

fetched live from OpenAlex

Class struggle is a necessary aspect of society. While ordinary people engage in struggles to improve their conditions, economically powerful people engage in struggles to defend their privileges. Thus, class struggle is from below and from above. And, class struggle occurs over interests and over ideas. To reproduce capitalism, it is not enough that resources be in the hands of the top 1%–10% thus economically forcing the vast majority to rely on wage work, or that police be used against their picket lines. It is also necessary that a large number of common people must possess ideas that make them accept the existing mechanisms of society as natural or as inherently good for all. But, these ideas are challenged too, which is how ideological class struggle from below happens. Academia is a major site of ideological struggle. Generally, professors propagate ideas that justify the reproduction of capitalism as it is or in slightly modified forms. These ideas can be challenged by students. The main aim of this article is to briefly discuss the nature of ideological class struggle in academia and to present a series of questions from the standpoint of the students who can oppose many of the ideas circulating in academia.

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)0.006
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 score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0150.066
Scholarly communication0.0120.009
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.046
GPT teacher head0.389
Teacher spread0.343 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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