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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".