Successful women of the Americas: the same or different?
Bibliographic record
Abstract
Purpose The intent of this cross‐national research is to study the personal and cultural characteristics of successful professional women. High‐achieving women may share certain personal characteristics, beliefs, and experiences, regardless of the countries in which they live. However, every individual is socialized within a particular national culture, and may be expected to share certain values and expectations with other members of that culture. Design/methodology/approach Over 1,100 professionally “successful women” (including high‐level managers, entrepreneurs, academics, government personnel, and professionals) and 531 undergraduate business students in nine countries – Argentina, Brazil, Canada, Chile, Mexico, the USA and the West Indies (Barbados, Jamaica, St. Vincent, and the Grenadines) completed surveys containing two sets of variables: national/cultural (collectivism/individualism, power distance, uncertainty avoidance) and personal (self‐efficacy, locus of control, need for achievement). Findings There were significant differences in the personal characteristics between successful women and the student comparison samples, with successful women consistently higher on self‐efficacy and need for achievement, and more internal on locus of control. There were some significant but smaller than expected differences in cultural characteristics between national samples. Originality/value This contrast of successful women living in the Americas provides new insights for managers of international companies seeking to be gender inclusive.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".