Participation in Helping Networks As Social Capital Mobilization: Impact on Influence for Domestic Men, Domestic Women, and International MBA Students
Bibliographic record
Abstract
This study examines participation in helping networks among MBA students and its impact on subsequent ratings of influence by peers. Helping networks reflect the mobilization of social capital where network contacts exchange social and material resources. As such, helping networks are distinct from friendship networks, which represent access to social capital but not necessarily its use. We identify three dimensions of social capital mobilization with different effects on status, specifically, mutual helping, nonmutual help giving, and nonmutual help receiving. Findings indicate that social capital mobilization through nonmutual help giving is a positive predictor of influence among peers at a later point in time. Nonmutual help receiving and mutual helping are unrelated to influence when nonmutual help giving is controlled. Gender moderates this relationship, but international student status does not. Nonmutual help giving does not enhance the perceived influence of women, particularly among domestic men. These findings support theories of status devaluation for marginalized groups and have implications for the value of the MBA for female students relative to their male peers. Future research on the predictors and outcomes of social capital mobilization can enhance understanding of the organizational experiences of diverse identity groups.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".