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Record W2167299150 · doi:10.1177/2158244015604693

Computer Users Do Gender

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

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.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.027
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0050.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.003

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations13
Published2015
Admission routes2
Has abstractyes

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