Are entrepreneurs penalized during job searches? It depends on who is hiring
Bibliographic record
Abstract
Abstract Research Summary How do job‐applicants with entrepreneurship experience—“post‐entrepreneurs”—fare in the wage labor job market? We propose an “entrepreneurship‐experience penalty” generally occurs yet varies in strength depending on the recruiters faced by post‐entrepreneurs in their job application process. In an experiment utilizing the selection‐decisions of 275 recruiters (experimental study participants) in reaction to objectively‐identical job‐applicants' resumes whose differences relate to whether their last‐held job was as a Founder or as an Executive , we found that: (a) resumes of Founders (compared to Executives) are about 23%–29% less likely to be picked as top‐choice for hire, (b) this entrepreneurship penalty is weaker for recruiters with (rather than without) entrepreneurial aspirations, and (c) this recruiter moderator‐effect is stronger for recruiters in smaller (rather than larger) firms.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".