Gender, Ethnic, and Functional Diversity’s Effect on Venture Performance: A Dynamic Venture Life-cycle Perspective
Bibliographic record
Abstract
There is contradictory and inconclusive evidence about the roles of gender, ethnic, and functional diversity in venture performance. Some studies show positive effects and others negative, but most studies use different venture performance measures, making it difficult to draw general conclusions. This thesis seeks to clear up contradictions and help explain prior findings by introducing a life-cycle stage perspective, using a variety of venture performance measures as integration points that map the different goals and objectives of ventures in each stage. Twelve hypotheses capture predictions about gender, ethnic, and functional diversity effects on venture performance through the dynamic effect of venture life-cycle stages (start-up, growth, maturity, and decline and/or innovation). I use a sample of high-tech ventures that participated in a Techstars accelerator program between 2007 and 2018 to test the hypotheses. Results suggest that looking at gender, ethnic, and functional diversity effects on venture performance through the dynamic lens of life-cycle stage theory makes it possible to generalize findings and come up with more precise conclusions about how each of the three types of diversity affects venture performance during each life-cycle stage.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".