Recalibrating Entrepreneurship Research: Decolonizing and Embracing the Pluralism of Entrepreneurial Activity
Bibliographic record
Abstract
Abstract Entrepreneurship research has long focused on exceptional, high‐growth, venture‐funded firms while overlooking the everyday and modest ventures that make up most entrepreneurial activity. This Special Issue calls for a recalibration of the field by decolonizing its assumptions and embracing its pluralism. We distinguish between conventional entrepreneurship, shaped by ideals of technology‐driven innovation and venture‐capital funded growth, and unconventional entrepreneurship, which reflects diverse and contextually grounded practices. Focusing on everydayness, pluralism, and decolonization, we draw on Santos’ concept of abyssal line to invite a shift from studying outliers to studying the ordinary. Using the metaphor of moving from a microscope to a prism, we call for theories that capture the full spectrum of entrepreneurial life across contexts and cultures. We discuss how papers in this Special Issue exemplify this prism approach and, in doing so, cast new light on how entrepreneurship can be understood, studied, and imagined.
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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 | 0.034 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.075 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".