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Record W3088506330 · doi:10.1521/siso.2020.84.4.458

Creative Labor, Mental Objects and the Modern Theory of Production

2020· article· en· W3088506330 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience & Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCreativityDivision of labourCommodityProduction (economics)Object (grammar)Value (mathematics)Information and Communications TechnologyCreative industriesSociologyEconomicsPsychologyComputer sciencePolitical scienceSocial psychologyMarket economyLawArtificial intelligenceMicroeconomics

Abstract

fetched live from OpenAlex

Recent decades have seen a proliferation of literature on creativity, with no consensus about what it consists of. Chinese and Russian contributions throw new light on these debates because of their concern with economic and human development. By integrating this with the widely-used concept of the “creative industries,” a rigorous concept of creativity rooted in the notion of creative labor is proposed. This can be defined as non-mechanical labor which, in conjunction with Information and Communication Technology (ICT), has produced a mass market in products embodying the use-value of distinctness. The creative industries then emerge as a branch of the division of labor making intensive use of creative labor in combination with mental objects, such as scientific and artistic products. Software, itself a mental object, is an “instrument of mental production” in these industries, helping explain their potential contribution to human development, and the obstacles to this potential imposed by the commodity form.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.283
Teacher spread0.246 · 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