Institutions and International Entrepreneurship
Bibliographic record
Abstract
In this study, we rerun and extend the confirmatory factor analysis (CFA) first analyzed by Busenitz, Gomez, and Spencer (2000). As in the original study, we develop a 3-factor model for the Country Institutional Profile (CIP) for entrepreneurship. This measure is designed to assess the institutional makeup of a given country and its population in terms of three domains: regulatory (state policies and legal frameworks), cognitive (shared social knowledge), and normative (common value systems). More specifically, the measure focuses on how each of these domains relates to entrepreneurship. In addition, we test several competing models with different factor structures based on institutional theory. We conclude that the 3-factor model presented in the original study provides the best fit for the data. However, we also caution that it only affords a modest fit and does not provide invariance across the countries tested. In its current state the instrument may not prove directly useful for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".