Preparing for a Day that May Never Come: Venturing in Limbo
Bibliographic record
Abstract
Abstract The new venture creation process is a central phenomenon in entrepreneurship research. Typically, scholarship has sought to identify common, linear stages of development in this process in pursuit of a sustained, growing venture. In contrast to this theory, this study reveals dynamic, non‐linear venturing processes that allowed for venture persistence despite failing to ‘progress’ toward traditional outcomes. We generate these insights from qualitative data on Syrian refugee entrepreneurs seeking to create and sustain ventures in Lebanon while living in a state of limbo – a precarious situation where the future is unknown and unknowable. We organize our findings in a model of venturing in limbo, which explains why and how entrepreneurs persist in venture creation practices despite experiencing repeated and significant setbacks that return them ‘to square one’. We reveal dynamic venture creation processes that allow for adaptive responses to erratic environmental shifts by producing entrepreneurial readiness, which consists of behavioural, cognitive, and psychological/emotional capabilities. Entrepreneurial readiness enables persistence of venturing efforts in the face of chronic precarity. Our study contributes to theory on new venture creation in entrepreneurship and organizational liminality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".