Entrepreneurs' growth aspirations and gender bias in investment decisions
Bibliographic record
Abstract
In this experimental study seeking to contribute to a better understanding of gender effects in new venture funding, we investigate the direct and interactive effects of an entrepreneurs’ gender, growth aspirations, and signalling of experience on potential investors’ funding decisions. To do so, we conduct a short online experiment where we ask participants (Angel investors from USA, UK and Canada) to watch a video of an actor playing the role of an entrepreneur making a 2-minute pitch designed to attract potential investors’ interest, after which we ask them to answer a few questions targeting their interest in potentially meeting the entrepreneur and investing in the venture. The study design is a two (gender: female / male) by two (growth aspirations: high / low) by two (signalling of experience: high / low) between-subject experimental design, we manipulate the video pitches such that we present different participants with different videos of the same basic venture idea and funding request, albeit with entrepreneurs of different gender, growth aspirations and levels of experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 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 teacher head, 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".