Job and industry fit: the effects of age and gender matches on career progress outcomes
Bibliographic record
Abstract
Abstract Using a sample of 232 MBA alumni, we tested the impact of respondent age, gender, and their interaction on career progress outcomes (managerial level, number of promotions, and salary) and whether age‐ and gender‐type of contexts moderated these relationships. Women's salaries did not increase much with age, whereas men's salaries showed a marked increase with age. We also found a gender × job gender‐type effect on salary, such that women earned somewhat higher salaries in masculine‐typed jobs, while men earned considerably higher salaries in feminine‐typed jobs. In addition, we observed a three‐way interaction between gender, age, and age‐type of industry indicating that younger men received more promotions in old‐typed industries, while younger women received more promotions in young‐typed ones. Results are discussed in light of cognitive matching approaches and status characteristics theory. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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".