Bibliographic record
Abstract
As the breadth and empirical diversity of entrepreneurship research have increased rapidly during the last decade, the quest to find a "one-size-fits-all" general theory of entrepreneurship has given way to a growing appreciation for the importance of contexts. This promises to improve both the practical relevance and the theoretical rigor of research in this field. Entrepreneurship means different things to different people at different times and in different places and both its causes and its consequences likewise vary. For example, for some people entrepreneurship can be a glorious path to emancipation, while for others it can represent the yoke tethering them to the burdens of overwork and drudgery. For some communities it can drive renaissance and vibrancy while for others it allows only bare survival. In this book, we assess and attempt to push forward contemporary conceptualizations of contexts that matter for entrepreneurship, pointing in particular to opportunities generating new insights by attending to contexts in novel or underexplored ways. This book shows that the ongoing contextualization of entrepreneurship research should not simply generate a proliferation of unique theories – one for every context – but can instead result in better theory construction, testing and understanding of boundary conditions, thereby leading us to richer and more profound understanding of entrepreneurship across its many forms. Contextualizing Entrepreneurship Theory will critically review the current debate and existing literature on contexts and entrepreneurship and use this to synthesize new theoretical and methodological frameworks that point to important directions for future research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".