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Record W2751619136 · doi:10.1080/09502386.2017.1363260

Participatory art within, against and beyond financialization: benign pessimism, tactical parasitics and the encrypted common

2017· article· en· W2751619136 on OpenAlexafffund
Max Haiven

Bibliographic record

VenueCultural Studies · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsLakehead University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFinancializationSociologyPoliticsCitizen journalismReflexivityEpistemologyAestheticsSocial sciencePolitical scienceEconomicsLawPhilosophy

Abstract

fetched live from OpenAlex

This essay examines three critical artists who orchestrate participatory spectacles and experiences as a means of challenging neoliberal financialization, an overarching paradigm and process that is reshaping economics, politics, society and culture. Critical art, I argue, can offer us a particularly insightful vantage point on these transformations. This is not, as it is usually supposed, because art has some lofty transcendental perspective above society; rather, it is precisely because art is already so deeply integrated into finance and financialization. Not coincidentally, “participation” appears as a keyword for the restructuring of both art and finance today, which I argue is key to understanding the challenges, limits and potentials of even avowedly critical participatory art, with lessons for cultural production more broadly. In order to analyse and draw broader lessons from the three artworks in question, I identify each with a strategic paradigm: benign pessimism, tactical parasitism and the encrypted common. These strategies, I argue, emerge within, against and beyond financialization and can help complicate our approach to its complex cultural politics.

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)0.011
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.089
Scholarly communication0.0120.009
Open science0.0010.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.311
Teacher spread0.211 · 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 designQualitative
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

Citations9
Published2017
Admission routes2
Has abstractyes

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