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Record W3127754669 · doi:10.1080/09540253.2021.1884197

(Dis)embodied masculinity and the meaning of (non)style in physics and computer engineering education

2021· article· en· W3127754669 on OpenAlexaffabout
Andreas Ottemo, Allison J. Gonsalves, Anna Danielsson

Bibliographic record

VenueGender and Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsMcGill University
Fundersnot available
KeywordsMasculinityEmbodied cognitionTranscendental numberStyle (visual arts)SociologyRationalityCompetence (human resources)EpistemologyMeaning (existential)AestheticsGender studiesPsychologySocial psychologyPhilosophyArtVisual arts

Abstract

fetched live from OpenAlex

Physics- and computer-related disciplines are strongly male dominated in Western higher education. Feminist research has demonstrated how this can be understood as reflecting a strong privileging of mind and rationality (over body/nature/emotions) in these disciplines, which harmonises with broader notions of masculinity as transcendental and disembodied. However, as we demonstrate in this paper, being recognised as legitimate in these fields is also tightly connected to embodiment. Drawing on post-structural gender theory, we explore how notions of corporeality, style and aesthetics are articulated within computer engineering and physics settings at two higher education institutions, one in Canada, one in Sweden. Using empirical data from two case studies, we demonstrate that these disciplines are usually understood as ‘gender neutral’ by students but that interest and competence in these fields are simultaneously understood as embodied through neglect for style and corporeal aesthetics, in ways that contribute to the masculinisation of these fields.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.023
Scholarly communication0.0060.002
Open science0.0000.004
Research integrity0.0010.001
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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.

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

Citations30
Published2021
Admission routes2
Has abstractyes

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