Gender, human capital, and opportunity identification in Mexico
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand how gender affects the number and innovativeness of business opportunities identified by future entrepreneurs in Mexico. Design/methodology/approach Comparing social feminist theory and human capital theory, this study examines the effect that human capital has on opportunity identification among men and women in Mexico. The authors specifically examine the role of specific and general human capital in the opportunity identification process. A survey instrument was applied to 174 MBA students at a university in Northeastern Mexico. Findings This study shows the significant effect of specific human capital: people with greater prior knowledge of customer needs or problems tended to identify more opportunities; however, the probability of identifying opportunities with innovation increased when individuals had been exposed to different industries through prior work and entrepreneurial experience. Gender differences were not significant for either the number of opportunities identified or the innovativeness of such opportunities. Originality/value This study provides evidence of the effect that human capital and gender have on opportunity identification in Mexico and provides an explanation within a context that has not been studied previously.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".