The effect of prior knowledge and networks on the recognition of social entrepreneurial opportunities across different cultures
Bibliographic record
Abstract
Social entrepreneurship is of great importance to society as it tackles social problems and supports communities. With the goal of making a positive impact on society, social entrepreneurs must firstly recognize social entrepreneurial opportunities. This research field is, however, considered to be still in its early stages. Therefore, in order to contribute to this field, this research aims at answering the research question “how do prior knowledge and networks impact the recognition of social entrepreneurial opportunities?” by critically assessing existing literature and conducting a qualitative analysis with nine social entrepreneurs from Austria, Brazil, and Canada. Several theoretical implications can be drawn from the empirical results: (1) prior knowledge of local and not local social problems, competitors, and opportunities positively impact opportunity recognition. (2) Education has no direct impact on opportunity recognition. (3) Work and founding experience positively impact opportunity recognition by providing the above-mentioned knowledge dimensions and allowing entrepreneurs to build their networks. (4) Networks provide essential knowledge to entrepreneurs, complement their human capital, provide emotional support, and consequently positively impact opportunity recognition.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".