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Record W2792977433 · doi:10.1177/0308518x18754883

Underperformative economies: Discrimination and gendered ideas of workplace culture in San Francisco’s digital media sector

2018· article· en· W2792977433 on OpenAlexaff
Daniel Cockayne

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCapitalismSociologyFraming (construction)Context (archaeology)Affect (linguistics)DualismGender studiesPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Drawing on recent research in feminist and cultural economic geography, as well as queer and affect theory, in this paper I examine the construction of ideas of workplace culture in the context of digital media work in San Francisco. I argue that in this context, workplace culture is produced as an idea that functions to describe certain individuals and behaviors as in or out of alignment with the firm’s established and gendered norms. I frame these observations around a discussion of affect and emotion in the workplace through a critical examination of interviews with workers in this setting. Drawing on Ngai’s framing of confidence as the tone of capitalism, and Berlant’s notion of underperformativity, I emphasize the gendered and affective dimensions of accumulation in the digital media sector, and how ideas of culture are discursively and materially constructed rather than natural or existing prior to their circumstances of production. In a practical sense, reproductions of a culture–economy dualism implicate gendered and other forms of discrimination in the workplace in terms of hiring practices, uneven distributions of (often emotional and unremunerated) work, and how difference in the workplace is valued or undermined.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.235
Teacher spread0.209 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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