“I really didn’t have any problems with the male-female thing until …”: Successful Women’s Experiences in IT Organizations
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.
Bibliographic record
Abstract
The gendered nature of organizations limits women’s opportunities for advancement. While women have made inroads into many male-dominated jobs, studies suggest they can be marginalized within masculine workplace cultures. In this paper, we examine the experiences of eleven women who have had successful careers in the male-dominated information technology field, to explore their perceptions of the barriers and opportunities women face. We find that our respondents have a tendency to downplay the significance of gender, even as they provide evidence that gender has shaped their careers. We argue that their reluctance to see how gender conditions women’s careers, combined with the technical nature of their field, may have facilitated their success, even though these factors serve as barriers for other women, and prevent meaningful change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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