Career advancement and family balance strategies of executive women
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore coping strategies devised by executive women in family relationships to advance their career and to maintain career/family balance. Design/methodology/approach A qualitative methodology using a sample of 25 executive women explores career advancement and career/family balance strategies within work and family contexts. Findings Analysis produces multiple career advancement and career/family balance strategies, including professional support, personal support, value system, and life course strategies such as the “ordering” of career and family, negotiating spousal support, and whether to have children. Research limitations/implications Adaptive strategies facilitate engagement in career and family, even in challenging gender environments, encouraging continued research on executive women's advancement and career/family balance. The idiosyncratic nature of career/family balance calls for greater emphasis on the context and timing of career and family experiences. Practical implications The paper offers guidance to women seeking to combine executive career and family and to organizations committed to the advancement and retention of women. Originality/value The paper jointly explores career advancement and career/family balance strategies pursued by executive women in family relationships. It contributes to a growing body of research on the coping mechanisms and adaptive strategies underlying balance between career and family.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".