A systematic literature review of the influence of the university’s environment and support system on the precursors of social entrepreneurial intention of students
Bibliographic record
Abstract
Abstract This systematic literature review aims at understanding the influence of the university’s environment and support system (ESS) in shaping the social entrepreneurial intention (SEI) of post-secondary education students. Social entrepreneurs play an important role in the economic and social developments of the communities in which they operate, thus many post-secondary institutions are starting to encourage more students to engage in social entrepreneurial behaviour. Consequently, there is a need for systematic approaches to evaluate the impact of various motivational factors related to the university’s entrepreneurial ecosystem that could affect the SEI of students. Based on a systematic literature review and narrative synthesis of the antecedents of the SEI of post-secondary education students, the authors proposed a customized SEI model that modifies and extend the one proposed by Hockerts (Entrepreneurship: Theory and Practice, 2017) and Mair and Noboa (Social entrepreneurship, 2006). This study fills a gap in the literature by providing a methodology grounded in theory that can help universities to design their educational and other interventions aimed at encouraging more students to consider social entrepreneurship as a viable career choice after graduation.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".