Why Are Women Underrepresented in the American IT Industry? The Role of Explicit and Implicit Gender Identities
Why this work is in the frame
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Bibliographic record
Abstract
Gender inequality in the IT profession is an acute issue with major individual, societal, and national implications. In this study, we build on the individual differences theory of gender and IT and extend it to account for subconscious processes that may drive women away from IT university majors and IT career choices. We specifically theorize on how the asymmetric roles of explicit and implicit gender identity facets impact the major selection of men and women students and affect their decisions to pursue the IT profession. To do so, this study introduces the concept of implicit gender identity, defined as the degree to which men and women subconsciously, automatically, and uncontrollably associate themselves with the masculine and feminine gender groups, respectively. We obtained data from 185 pre-major selection university students by means of a survey and the Implicit Association Test. The findings revealed that implicit gender identity was a significant predictor of IT major and career choices for women but not for men university students. Explicit gender identity had no influence on IT major and career choices for men or women university students. Nevertheless, men’s and women’s IT major and career choices appear to be similarly influenced by normative pressures. IT skills and IT work experience also impact such choices. Ultimately, this study shows that implicit gender identity can be a factor that drives women university students away from the IT profession and contributes to the gender gap in the field.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it