Does The Gender Of The Manager Affect Who He/She Networks With?
Bibliographic record
Abstract
Based on a sample of 72 managers from Hong-Kong and1032 associates identified by these managers, the results show that female managers network with other females for expressive support but when seeking instrumental contents, they network with male associates. We also found that females are less likely to approach female associates they have strong ties with but are more likely to approach similarly ranked colleagues. They are also unlikely to approach higher ranked female colleagues to network on instrumental contents. Taken together, these results imply that for female managers seeking instrumental support, they should focus on peer-relationships with other females as well as on male associates with whom they have strong ties with. From a stakeholder’s point view, more attention should be paid to designing and implementing social policies and integrating a gender perspective into all public policies. This calls for setting up an integrated network of structure, mechanism and processes designed to arouse more gender-awareness, increase the number of women in decision-making role, facilitate the formulate of gender-sensitive policies and programs. Long-term strategies should be developed to build up women through personal growth process, promote integration and equality in the workplace.
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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 | 0.002 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".