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Record W1561191966 · doi:10.3138/topia.30-31.65

Games and the Subjugated Knowledges of Finance: Art and Science in the Speculative Imaginary

2014· article· en· W1561191966 on OpenAlexvenueno aff
Rob Aitken

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsExpansiveFinancePoliticsContext (archaeology)Social studies of financeThe ImaginaryOrder (exchange)Public financeSociologyRationalityEconomicsPositive economicsPolitical scienceLawHistoryPsychology

Abstract

fetched live from OpenAlex

Liberalized financial markets now seemingly shape the very contours of global political-economic life and the forms of self and citizen most common to our political present. It is in this context, however, that the culture of finance is often depicted in overly expansive, reified terms. Are there limits to finance and the “financialized imagination” as it has taken hold over the past century? Are there borders to the kind of reach and influence finance now exerts on our political economy? This paper addresses these questions by reviewing the ways in which recent public art has attempted to reclaim what, following Foucault, I refer to as the “subjugated knowledges” of finance: forms of knowledge such as gaming and game-play, which were removed from the discourse of finance when it was recast as rational and scientific over the 19th and 20th centuries. The paper considers several public art interventions that engage with gaming, game culture or game-play of various forms, in order to disrupt financial practices and abstraction. If limits are to be (re)imposed on finance, I conclude, we need critical strategies which can reclaim the subjugated knowledges of finance and which can, by extension, question the self-image of finance as a rational domain.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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 designObservational
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

Citations3
Published2014
Admission routes1
Has abstractyes

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