Who Can Tell? Network Diversity, Within‐Industry Networks, and Opportunities to Share Job Information
Bibliographic record
Abstract
This article examines opportunities to share job information. It adds to the growing body of research on information holders and complements existing research that explains what kinds of networks and network positions provide the greatest benefit to job seekers. Data from an exploratory study of entry‐level, white‐collar workers are used to relate opportunities to share information—defined to consist of both knowledge of a job opening and awareness of a potential applicant among one's network members—with information holders’ network composition. The data show that information holders with strong within‐industry networks have more opportunities to share information and do share more information. Information holders with diverse networks more often identify potential applicants for jobs and thus have more opportunities to share information. However, despite having more opportunities to do so, they do not share information more often than those with less diverse networks. These findings, combined with the growing literature on information holders, suggest that different aspects of network composition affect the flow of job information at different stages and thus by different mechanisms.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".