Cross‐gender networking in the workplace: causes and consequences
Bibliographic record
Abstract
Purpose This study seeks to examine how individual and organizational characteristics as well as attitudinal factors can affect the network composition of female managers. Another of its objectives is to examine the effect of cross‐gender network on the quit intention of female managers. Design/methodology/approach A survey questionnaire was administered, seeking information on the personal characteristics and attitudes of the 91 managers, the characteristics of the organization for which the respondent works, and the network characteristics of the respondents in Hong Kong. Findings The results show that positive attitudes towards women's leadership qualities and higher ratio of females in top management positions are associated with a lower cross‐gender instrumental network for females. Perceived discrimination or being married encourages female managers to seek a cross‐gender network. Cross‐gender networks reduce the quit intentions of female managers. Originality/value The study offers a better understanding of how networks change involves an examination of both the characteristics of the network holder and the larger context in which the network holder is located. It contributes to the scant evidence on the consequences of cross‐gender networking for female managers in the Chinese context.
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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 | 0.003 |
| 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) | 0.005 | 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".