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Record W4404883874 · doi:10.1177/21582440241267157

Passion as Capital: The Cultural Production of “Good Computer Scientists”

2024· article· en· W4404883874 on OpenAlexafffundabout
Hana Darling-Wolf, Elizabeth Patitsas

Bibliographic record

VenueSAGE Open · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPassionProduction (economics)Cultural capitalCapital (architecture)SociologyPsychologyClassical economicsPolitical scienceSocial scienceEconomicsSocial psychologyArtVisual artsMicroeconomics

Abstract

fetched live from OpenAlex

“Do what you love” has become a hegemonic mantra in large swaths of the labor force, and the expectation for workers to be passionate about their work is intense within the technology sector. When we interviewed eight undergraduates at a large Canadian university about what makes someone a “good computer scientist,” participants stressed the importance of passion. Passion was described as a form of capital: to be accumulated and traded for advancement in the labor market. We examine how passion works as a hegemonic norm within this culture, and untangle passion as requiring two distinct processes: conspicuous production (via extracurriculars and internships), and affective passion (via performing interest and enthusiasm for the subject). We explore the relationships and feedback loops amongst conspicuous production, affective passion, and other forms of Bourdieusian capital. In doing so, we uncover the mechanics of the power structures which underlie post-feminist and meritocratic narratives of self-motivated passion.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.275

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 studies0.0000.000
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 head0.038
GPT teacher head0.360
Teacher spread0.322 · 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 designNot applicable
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

Citations0
Published2024
Admission routes3
Has abstractyes

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