Applying a Gender Lens to the Predictors of High-tech Career Intentions among Engineering Students in Bangladesh
Bibliographic record
Abstract
This paper explores the extent to which perceived job attributes, perceived male dominance in the high-tech sector, and perceptions of the media’s gendered representation of high-tech might influence students’ intentions to pursue a career in the high-tech sector. A survey was conducted with 209 female and 640 male engineering undergraduate students in Dhaka, Bangladesh. The results suggested that both female and male students were attracted to high-tech when they viewed it as a challenging career. Gender role stereotypes also, however, influenced the career intentions of both women and men. Although they are influenced by different types of gendered norms – women by attitudes toward the suitability of high-tech careers for women and men by male media images of high-tech – the gendering of high-tech work influenced both women and men. The results contradict previous findings that female students perceive high-tech work as boring, uncool, and nerdy but support previous findings on the negative effect of gender stereotyping on female students’ interest in pursuing a high-tech related career
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".