Legitimacy Spillovers and Piggybacking: The Collective Legitimation of New Ventures and Fields
Bibliographic record
Abstract
All new ventures face the liability of newness, and even more so in nascent entrepreneurial fields. This pressure is attenuated by the acquisition of legitimacy, which is consistently highlighted as beneficial in the literature. Yet, little attention has been given to the downsides of acquiring legitimacy or the benefits of failing to do so. Conceptualizing entrepreneurship as a distributed and collective process, this paper examines how prior successes and failures shape future legitimation efforts. We first argue that legitimation efforts are contingent on the ability of entrepreneurs to attract new audiences. Considering both successes and failures, we distinguish legitimation efforts that generate “legitimacy spillovers” from those that do not and argue that these spillovers enable entrepreneurs to “piggyback” off the legitimation efforts of others. We test our hypotheses by examining more than 180,000 crowdfunding campaigns launched on Kickstarter, one of the most important crowdfunding platforms. In doing so, our work contributes to cultural and constitutive perspectives on entrepreneurship.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".