Business as usual? How Entrepreneurs Adapt to Cumulative Adversity
Bibliographic record
Abstract
Survival under adversity is an ongoing, effortful accomplishment: entrepreneurs continuously counter-act the sudden and often significant vulnerability of their venture to external shocks and/or chronic crises. While quantitative studies paint a bleak picture by the numbers of the entrepreneurs who exit and fail, qualitative research recognizes that different types of adversity may either strain or strengthen the entrepreneurial-venture relationships. Using an inductive study of entrepreneurs traversing a 12-window year of unprecedented political turbulence, we develop a theoretical model of continuance under cumulative adversity that delineates how entrepreneur’s orientation towards the pain of others or their own relates to the survival paths they choose to follow to prolong the life- span of damaged and rapidly declining ventures. We contribute a processual understanding of survival and differentiate between paths of persistence, endurance and reflexive perseverance. By inducing the twin notions of progressive and regressive failure and explaining how they unfold over time, we also complement extant theories of business continuance in extreme environments.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".