Modeling Intention to Pursue a High Tech Career Using Social Cognitive Career Theory
Bibliographic record
Abstract
This study is the first to apply Social Cognitive Career Theory (SCCT: Lent, Brown, & Hackett, 1994, 2000) to predict engineering students' intention to pursue a high-tech career in a South Asian context.The purpose of this study was twofold.The first objective was to test SCCT's interest and choice model in a non-Western context in order to test the applicability of the theory.The second objective was to expand beyond the core of the theory to incorporate background and proximal, contextual gender related variables such as gender-role orientation, masculine image of high-tech professionals, and gendered perceptions.The data came from undergraduate engineering students studying in different universities in Bangladesh.Data were collected through self-administered pencil and paper survey.A total of 976 valid surveys were used in the data analysis.As this study was based on a new context -Bangladesh, the construct validity of the measurement scales was assessed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).EFA and CFA should not be performed on the same data set; the data were randomly split into two groups for this analysis.Structural equation modeling (SEM) was used to assess model fit for the whole sample.Results indicated that social cognitive theory was a good fit for the data.The multivariate analysis indicated that all of the core SCCT predictors -occupational self-efficacy, outcome expectations, and interest -were important in explaining intentions.Gender-role orientation also played a significant role although gender related variables did not perform as expected.Also, contrasting findings regarding environmental variables of social support and barriers
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".