9. Does Social Capital Pay Off More Within or Between Ethnic Groups? Analysing Job Searches in Five Toronto Ethnic Groups
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We compare the job search experiences of five Toronto ethnic groups: English, German, Jewish, Ukrainian, and Italian-Canadian: We study the kinds of job contacts that members of different ethnic groups have used and the income they earned in these jobs. Our key questions are: (1) To what extent do members of each group use ties within their own ethnicity or outside of it to search for jobs? (2) Which ethnic groups attain higher incomes when their members use job contacts within or outside of their own ethnicity? We find that members of low-status ethnic groups tend to achieve higher income when they have ties outside of their own ethnic group. By contrast, members of high-status groups tend to do better when they have ties within their own group. Both gender and generation of immigration play complex roles in the nexus of ethnicity and network heterogeneity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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