Societal Ethics and Social Entrepreneurship: A Cross-Cultural Comparison
Bibliographic record
Abstract
Using multilevel modeling and data from 26 countries that include 93,439 individual-level responses on social entrepreneurship for the year 2015, we seek to understand how societal-level ethical orientations impact the likelihood of individuals engaging in social entrepreneurship. We develop a multidimensional representation of societal ethics, in that we draw close parallels between the three institutional pillars—normative, cognitive, and regulatory—with three forms of ethics and use this understanding to predict their effects on the demand for and supply of social entrepreneurs. We find that low behavioral ethics (normative ethics) at the societal level provides opportunities for individuals to become social entrepreneurs. Furthermore, while unselfishness (cognitive ethics) motivates individuals to become social entrepreneurs, high public-sector ethics (regulatory ethics) provides the institutional support for such entrepreneurs to thrive. We contribute to cross-cultural comparative entrepreneurship by providing ethical antecedents of social entrepreneurship through a deeper understanding of the influence of ethics as national-level institutions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".