Does Social Capital Pay Off More Within or Between Ethnic Groups? Analyzing Job Searchers in Five Toronto Ethnic Groups
Bibliographic record
Abstract
nic groups attain higher incomes when their members use job contacts within or outside of their own ethnicity? Building on Previous Research One stream of research in social network analysis has investigated what characteristics of ties and networks help people to obtain information and find jobs. Mark Granovetter first showed that weak ties are important for obtaining professional-level jobs (1973, 1974/1994,1982). He argued that because weak ties are more apt than strong ties to connect people to different social circles, they are more apt to provide new information (about jobs). Yet other scholars have argued that when information is scarce and valued, strong or high-status ties are prime sources of information and jobs (Campbell, Marsden and Hurlbert 1986; Lin and Dumin 1986). For example, it is close kin and good friends who give poor Chileans information about scarce jobs (Espinoza 1999). Although researchers -2have subsequently argued that it is the heterogene
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".