Gender effects of project assessment: Evidence from a market simulation
Bibliographic record
Abstract
We investigate how males and females perform as entrepreneurs and traders using information on the trading activities of students participating in a business game in two university courses. In one course, students create entrepreneurial ventures that they pitch to their peers. These students are issued securities of all the ventures and trade in a simulated market based on information revealed in the classroom pitches. In the second course, students trade these ventures in a separate simulated market but do not see pitches and trade based on anonymized written information about the ventures. We measure student performance as entrepreneurs by the traded prices of their proposed ventures in the online market and performance as traders by the value of their closing portfolios. In the course where traders observe the sales pitches of the entrepreneurial teams, we find that both male and female traders buy and sell at lower prices when the female share of the venture team increases. Females buy at higher prices and sell at lower prices than males and end up with lower portfolio values than males. None of these results obtain in the course where trading is based on the same information delivered in written and anonymized form and the gender composition of the venture teams is not known. These findings provide insight on how the assessment and performance of tomorrow's business leaders is affected by environments involving direct sales pitches.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".