Gender and careers: a study of persistence in engineering education in Bangladesh
Bibliographic record
Abstract
Purpose The goals of this study were to examine the utility of social cognitive career theory in a South Asian context, extend SCCT beyond its individualistic roots to include social and contextual variables, and explore the possible differential validity of SCCT predictors for men and women. Design/methodology/approach The study involved an in‐class survey of Bangladeshi undergraduate engineering students including 209 women and 640 men. Findings Despite stronger relationships between persistence and two predictors – social aspirations and self‐efficacy – for men, self‐efficacy, the core construct of SCCT, was the most important predictor of persistence for both women and men thus supporting the applicability of SCCT in non‐Western contexts. Research limitations/implications Several new measures were developed for this study which provide a basis for future research but will require further validation. The results demonstrated the applicability of SCCT in a non‐Western context but the amount of variance explained was modest. Thus, additional research into context‐specific factors affecting persistence is warranted. Practical implications The results suggest that interventions intended to enhance the participation of women in non‐traditional fields such as engineering should focus on enhancing self‐efficacy, potentially through creating a more supportive learning environment. Originality/value The current study is one of the first to assess the applicability of SCCT in a non‐Western context and to examine the differential validity of SCCT predictors for women and men.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".