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Record W4394786214 · doi:10.1177/20594364241247675

Twitch spouse: Livestreaming and the legacy of spousal labour in the video game industry

2024· article· en· W4394786214 on OpenAlexafffundabout
Christine H. Tran

Bibliographic record

VenueGlobal Media and China · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSpousePrecarityEntertainment industrySociologyEthnographyEntertainmentVideo gameSpace (punctuation)Gender studiesPolitical science

Abstract

fetched live from OpenAlex

Precarious careers in the games industry have long relied on the unpaid and largely feminized support of spouses and family members. This paper addresses the role of spouses and other domestic cohabitants in the production of live game broadcasts on Twitch, Amazon’s world-leading platform in live video entertainment. I introduce the heuristic of the ‘Twitch Spouse’ to underscore the crucial role that domestic partners have played as invisible workers in the wider games industry, whose precarious conditions have been extended by the rise of at-home livestreaming. Drawing from ‘playful’ interviews and ethnographic observation with 12 Twitch creators located across the United States and Canada, I delineate three themes by which the partners of Twitch streamers vitally contribute to livestreaming: collaborative space production, the management of intimacy, and timekeeping. Herein, I show how a theorization of the ‘Twitch Spouse’ will build future pathways for recognizing the intertwined struggles of domestic and digital work within the precarious horizons of the game industry. This paper argues that Twitch streamers’ conceptualizations of intimate partners’ supportive labour reinforce domesticity and visibility as co-extended forces in the evolving relevance of digital labour to contemporary capitalism.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.288
Teacher spread0.276 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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