Entrepreneurial Ecosystems for Students (EES): Organizing Framework and Evidence Across Countries
Bibliographic record
Abstract
This panel will form a timely discussion to advance the emerging research stream on entrepreneurial ecosystems for students (EES). The creation of a new venture by students or recent alumni has become an increasingly important phenomenon in entrepreneurship. The growth of student entrepreneurship is usually boosted by a mixture of mechanisms internal and external to the universities and the outcome of student entrepreneurship is often a function of the entire ecosystem. Studies on ecosystems for student entrepreneurship have revealed a great degree of heterogeneity across countries or regions. Yet there is a lack of overall organizing framework to understand the heterogeneity. This panel will draw from empirical studies and cases in different country settings in America, Asia, and Europe and reflect on the overall conceptual framework for EES and the methods to study it. Each of the panelists will introduce her/his research on the topic, bringing in the empirical evidence in different countries, followed by a discussion on the commonalities and distinctions across ecosystems and the organizing framework for studying them. The key data and methods that researchers are using in studying EES will also be reviewed during the session.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".