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Record W3043356506 · doi:10.1386/jfs_00009_1

Productive leisure in post-Fordist fandom

2020· article· en· W3043356506 on OpenAlexaff
Aleena Chia

Bibliographic record

VenueThe Journal of Fandom Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFandomFordismAmateurSociologyLiminalityConsumption (sociology)CapitalismTourismMainstreamMedia studiesAestheticsPolitical sciencePoliticsSocial scienceLawEconomyEconomics

Abstract

fetched live from OpenAlex

For over a decade, scholars have considered how digital play has converged with the work of media production. From esports and volunteer moderation to play-testing, the circuits of game production are accelerated by players’ passionate engagements as fans and hobbyists, which are intertwined with their professional ambitions to join the industry. It is now taken for granted in scholarly discourse that work and play, production and consumption, and professional and amateur identities are blurring. Researchers propose hybrid terms such as ‘prosumption’ or ‘playbour’ to capture the variation, complexity and contradictions in media participation and value creation across diverse fan practices. This analysis proposes that these post-Fordist neologisms oversimplify techno-cultural changes and legitimate ambiguities in fans’ relationships with media companies and their imperatives for productivism in platform capitalism and its gig economies. In contrast, hobbies have always been a mediating category of productive leisure that can be traced back to industrialization’s cleavage of labour from recreation. This article argues that charting how this liminal category of hobbies has been institutionalized in contemporary media practices provides an analytical lens to interrogate post-Fordist obligations of productivity and neo-liberal expectations of entrepreneurialism.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.056
GPT teacher head0.341
Teacher spread0.285 · 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 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

Citations13
Published2020
Admission routes1
Has abstractyes

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