Network Capital in a Multi-Level World:
Bibliographic record
Abstract
Multi-level analysis provides a new approach to studying the sources of network capital by integrating analyses of individuals, interpersonal ties and the personal networks in which they are embedded. Using this approach aids theory and substantive analysis. Toronto data show that while tie characteristics are key predictors of supportive behavior, networks facilitate the supportive behavior of ties and individuals. For example, parents and children are more supportive in networks with high percentages of parents and children. Individual agency, dyadic duets, and networkproperties all make network capital available for social support. Acknowledgments We are grateful to earlier collaborators in East York personal community research for the foundation laid for this study, to the Rockefeller Foundation for providing Wellman with a month's stay to complete this work at the magnificent Bellagio (Italy) Center for Study and Conferences, and to the University ofToronto's Centre for Urban and Community Studies for its thirty years of being an eminently supportive research base. The contributions of Milena Gulia, Catherine Kaukinen, Stephanie Potter and Scot Wortley have been especially important for our work here, as have been the comments of Dean Behrens, Bonnie Erickson, Vicente Espinoza, Nan Lin, Uwe Matzat, Pamela Popielarz, Ray Reagans, Fleur Thomrse, Charles Tilly, Beverly Wellman, and the members of the "Sooner" electronic mail discussion list. Earlier versions of this paper were presented to the Duke University Social Networks and Social Capital Conference (1998), the American Sociological Association (1999, 2000) and the International Sunbelt Social Network Conference (1999, 2000). Our research has been supported by grants to Barry Wellman from the Bell Canada Univers...
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".