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The AI Fetish: When Wooden Brains Begin to Think

2025· article· en· W4414402328 on OpenAlexaff
Alex Levant

Bibliographic record

VenueCaderno Brasileiro de Ensino de Física · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace Science and Extraterrestrial Life
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsHumanityAgency (philosophy)FetishismIndividualismHuman intelligenceMethodological individualismNeoliberalism (international relations)Social theory

Abstract

fetched live from OpenAlex

This article examines how artificial intelligence has become fetishized in contemporary discourse, being imagined as an autonomous force rather than crystallized collective human labor. Drawing on Marx's theory of commodity fetishism and critical AI studies, the paper analyzes how the apparent agency of AI systems masks their dependence on human labor. It thus challenges individualistic models of intelligence as computation, favoring an understanding of cognition as fundamentally social and historically situated, drawing on authors such as Vygotsky and Ilyenkov. The paper argues that the collective intelligence crystallized in these systems belongs to humanity as a whole, and must be reclaimed, since AI is too powerful for society to leave in the hands of the tech oligopoly. It advocates for a public AI under democratic control, recognizing that the struggle over AI is inseparable from broader struggles against exploitation, for climate justice, and for genuine democracy.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.758

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.292
Teacher spread0.282 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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