Does The Gender Of The Manager Affect Who He/She Networks With?
Why this work is in the frame
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Bibliographic record
Abstract
<p class="MsoBlockText" style="margin: 0in 34.2pt 0pt 0.5in;"><span style="font-style: normal;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">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.<span style="mso-spacerun: yes;">&nbsp; </span>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.<span style="mso-spacerun: yes;">&nbsp;&nbsp; </span>They are also unlikely to approach higher ranked female colleagues to network on instrumental contents.<span style="mso-spacerun: yes;">&nbsp; </span>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&rsquo;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.</span></span></span></p>
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it