A conceptual model and assessment criteria to inform gender-smart entrepreneurship education and training plus
Bibliographic record
Abstract
Purpose This study aims to problematize how gender is enacted within entrepreneurship education and training (EET). Design/methodology/approach Using a social feminist lens, this study advances principles, a conceptual framework, assessment criteria and illustrative performance metrics to inform gender-sensitive EET programs and courses. Findings are based on a cross-case thematic analysis of two large-scale case studies conducted in Canada and Jordan. Findings The findings bridge social feminist theory and EET studies. The originality of the research rests in its utilization of the principles and conceptual framework to examine EET and to inform the development, design and assessment of gender-sensitive programs and courses. Research limitations/implications The framework and criteria do not differentiate types or levels of EET. The investigators lead the assessment of curricula and co-construction of gender-sensitive course content. Interpreter bias cannot be ruled out. Practical implications The proposed principles, framework, criteria and performance will assist stakeholders in EET program/course design, content, delivery and evaluation. Social implications Aligned with the United Nation Sustain Development Goal 5 (gender equity), the findings demonstrate the value of adapting a critical lens across all elements of EET and responding to biases in participant selection and engagement, program design and curricula. Originality/value To the best of the authors’ knowledge, this is among the first studies to use a social feminist perspective and case study methodology to inform criteria to assess EET.
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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 | 0.041 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".