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Record W2529792211

Why are Women Underrepresented in IT? The Role of Implicit and Explicit Gender Identity

2016· article· en· W2529792211 on OpenAlex

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.

Bibliographic record

VenueAmericas Conference on Information Systems · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsLakehead University
Fundersnot available
KeywordsIdentity (music)Implicit-association testNormativePsychologySocial psychologyGender identityField (mathematics)Test (biology)Implicit attitudeAssociation (psychology)Gender studiesSociologyPolitical scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

This study demonstrates that gender identity is an important factor affecting female university students' decisions to major in IT and join the IT profession. It introduces the concept of implicit gender identity, defined as the degree to which people unconsciously, automatically, and uncontrollably associate themselves with their biological sex. Data were obtained from 185 students by means of a survey and the Implicit Association Test. The findings reveal that gender identity plays different roles between men and women in its influence on IT major and career choices. Implicit gender identity is a strong predictor of IT major and career choices for women but not for men. Explicit gender identity influences IT career choice only for women. Males' and females' IT major and career choices are influenced by normative pressures to the same degree. This study shows that gender identity can be a reason driving women away from the IT field.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.612

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 communication0.0000.001
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 head0.047
GPT teacher head0.329
Teacher spread0.282 · 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