The Legitimacy Threshold Revisited: How Prior Successes and Failures Spill Over to Other Endeavors on Kickstarter
Bibliographic record
Abstract
How does the legitimacy conferred on entrepreneurial endeavors affect the legitimacy of subsequent ones? We extend the notion of a “legitimacy threshold” to develop and test a recursive model of legitimacy. Whereas extant research has focused on whether entrepreneurial endeavors garner sufficient support from key audiences to cross this threshold, we argue that the order of magnitude by which they succeed or fail is also consequential for later entrants. Distinguishing “blockbuster” from “unsung” successes, and “path breaking” from “broken path” failures, we contend that recent successes and failures affect related subsequent endeavors in predictable, though sometimes counterintuitive ways. We test our hypotheses by examining 182,358 entrepreneurial endeavors pitched within 165 categories over a six-year period on Kickstarter, one of the most important crowdfunding platforms. We show that individual outcomes, taken collectively, generate legitimacy spillovers, either by encouraging audiences to repeatedly support other related endeavors or by discouraging them from doing so. Our research contributes to understanding the recursive nature of legitimacy, the competitive dynamics of entrepreneurial efforts, and crowdfunding platforms.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".