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Record W2167299150 · doi:10.1177/2158244015604693

Computer Users Do Gender

2015· article· en· W2167299150 on OpenAlex
Lori Leach, Steven L. Turner

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSAGE Open · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsUniversity of New BrunswickGovernment of New Brunswick
Fundersnot available
KeywordsSociologyDigital divideInformation technologyInformation and Communications TechnologyPublic relationsComputer scienceGender studiesPolitical scienceWorld Wide WebLaw

Abstract

fetched live from OpenAlex

The so-called “digital gender divide” has encouraged studies attempting to demonstrate the co-production of gender and information technology. Vivian Lagesen has criticized many of these attempts for failing to provide fully symmetrical accounts. Here we describe and analyze beliefs and practices concerning computers, gender, and technology evinced by managers in a network of public sites (Community Access Centers) created to provide community access to digital technology in the Canadian province of New Brunswick. From those results, we argue, among other conclusions, that distinguishing more carefully between the gendered uses of new technologies and the gendered forms of attraction associated with them produces a more fully realized and more perfectly symmetric understanding of how gender and communications technologies are co-produced. We show that the concepts of actor-network theory facilitate that analysis, and so interpret the study as supporting and extending Lagesen’s program.

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.

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: none
Teacher disagreement score0.973
Threshold uncertainty score0.789

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.137
GPT teacher head0.409
Teacher spread0.272 · 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