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Record W2921935630 · doi:10.1163/1569206x-00001713

Capital’s Artificial Intellect Becoming Uber’s Means of Autonomous Immaterial Production

2019· article· en· W2921935630 on OpenAlexaff
Ramon Salim Diab

Bibliographic record

VenueHistorical Materialism · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Digital Transformation
Canadian institutionsWestern University
Fundersnot available
KeywordsIntellectInformation and Communications TechnologyCapital (architecture)Production (economics)AppropriationReproductionMeans of productionSociologyArtificial intelligenceComputer scienceBusinessIndustrial organizationEconomic systemEconomicsEpistemologyMarket economyPhilosophyHistoryHuman capitalFinancial capitalMicroeconomics

Abstract

fetched live from OpenAlex

Abstract The global path of capitalist development is continuously transformed as a result of the production and integration of advanced information and communication technologies (ICTs) within various forms of production. The first half of this paper conceptualises ICTs as capital’s appropriation and objectification of the productive forces of the general intellect in ‘the general artificial intellect’, a category that refers to the total processing power of networked ICTs in global society. The second half of the paper analyses Uber’s development of the elements of capital’s artificial intellect as the cybernetic means of real subsumption in its immaterial production process. The paper concludes that Uber’s development of autonomous vehicles is an example of the global trend in transport automation that could raise the organic composition of capital of the transport industry, which I suggest would advance the stage of real subsumption toward a third and final stage of autonomous subsumption.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.020
Scholarly communication0.0060.006
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.195
Teacher spread0.157 · 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
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
Published2019
Admission routes1
Has abstractyes

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