Doing and undoing gender: women professionals' persistence in technology occupations
Bibliographic record
Abstract
Purpose This study aims to identify women professionals' strategies to persist in the male-dominated technology industry situated in the Bangladeshi socio-cultural context. Design/methodology/approach In-depth interviews with women tech professionals were conducted to identify and explore the strategies. Thematic coding was used for data analysis. Findings The findings suggest that the complex interplay of macro-, meso- and micro-factors pushes women to defy societal and gender norms in their choice and persistence, yet they simultaneously conform to these norms. By simultaneous expressions of doing and undoing gender, these women dealt with hierarchies and inequalities, navigated masculinized industry and empowered themselves within a patriarchal culture. The strategies effectively allowed them to demonstrate agency and persist in tech occupations. Research limitations/implications The study participants were women and recruited using snowball sampling. Future research could benefit from recruiting a larger, more varied sample using random sampling. Practical implications The study can inform teaching and policy initiatives to increase women's representation in tech sectors through awareness campaigns, policy interventions and counseling. Originality/value The research extends the doing and undoing framework by integrating the relational perspective to explain women's agency and resilience situated in a patriarchal context. The paper focuses on women's micro-individual strategies to navigate macro- and meso-level forces. Moreover, Bangladesh is an under-researched context, and findings from the study can help design potential intervention strategies to increase women's participation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".