Hypergrowth Exit Mindset: Destroying Societal Wellbeing through Venture Capital Biased Social Construction of Value
Bibliographic record
Abstract
Exit! Exit! Exit! Our innovation ecosystems are focused on this goal above all else, thanks to the reliance on venture capital. Young potential entrepreneurs talk about exit strategies before even creating an innovation or starting a business. Our innovation ecosystems push them to do so in many ways. Seemingly straightforward questions to budding entrepreneurs such as “What is your exit strategy?” drastically shift focus and outcomes away from creating long–term societally beneficial innovations. We argue that this hypergrowth exit mindset is destroying societal wellbeing due to its laser focus on increasing socially constructed exit value above all else. As a field we need much more research about the antecedents and consequences of the mindset, as well as research informed alternative innovation models. To address this fertile research area, we call for more research on alternative models of innovation built upon societal wellbeing as opposed to exit valuations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".