Gender differences in involuntary job loss and the reemployment experience
Bibliographic record
Abstract
Purpose The study seeks to compare the experiences of job loss and reemployment experiences among female and male higher level managers and professionals. Design/methodology/approach The paper compares data collected at two periods in time from (n=120) females and (n=184) males who completed two self‐report questionnaires. Findings Relatively few gender differences were observed in the present study. The fact that females and males experienced the job loss and re‐employment process similarly was interpreted as a sign of progress. Main differences were found in networking and personality types, with men being more successful in networking and less agreeable types. Research limitations/implications This is a self‐report study and somewhat smaller sample at time two. Secondly, some of the findings may not generalize to those outside of outplacement. Practical implications Outplacement services may use these findings in guiding their counseling practice and focusing more on helping female executives in their networking efforts for example. Originality/value This paper contributes to the gender literature by looking at experience of job loss and reemployment for a particular and rarely examined group of individuals. It offers new knowledge on gender differences among executives and higher level managers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".