Sex-based labour market segregation and women's perceptions of entrepeneurship
Bibliographic record
Abstract
Sex-based labour market segregation and women's perceptions of entrepeneurship Here Professors Tonoyan, Strohmeyer, and Jennings investigate sex-based labour market segregation and women's perceptions of entrepreneurship. As noted in a prior Open Access Government article, women tend to participate in entrepreneurial activity at lower rates than men within most countries included in the Global Entrepreneurship Monitor. Numerous plausible reasons for this gender gap exist. A large-scale study by Professors Vartuhi Tonoyan (California State University, Fresno), Robert Strohmeyer (University of Mannheim), and Jennifer E. Jennings (University of Alberta) put forth and examined the argument that women are likely to possess less favourable perceptions than men, on average, of how easy it would be to start a business. These scholars further argued that this disparity can be attributed to sex-segregated positions within traditional wage-and-salary employment, which present structural disadvantages for women’s 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".