Why Are Women Underrepresented in the American IT Industry? The Role of Explicit and Implicit Gender Identities
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".