Bibliographic record
Abstract
Abstract Our study drew on a data set of 513 Chinese immigrants that was collected in Toronto in 1998 and 1999. We looked at how prior experiences in the ethnic economy affect current job transitions. Our analysis went beyond previous studies by situating job transition in the context of the economic integration of immigrants, with consideration of possible competing job transition outcomes and previous recurrent job transitions. Descriptive information in the study showed evidence of both forms of job transition outcomes, that is, either shifting away from or staying in the ethnic economy. Based on the competing risks model of event history analysis, which simultaneously considers different job transition outcomes, the results confirm that prior experience in the ethnic economy increases the likelihood of job transition away from and also remaining in the ethnic economy. However, the results also clearly suggest that those who have a higher level of English language ability are more likely to shift away from the ethnic economy. Results from the repeated events model show that the recurrent job transition experience does change the effects related to job transitions within the ethnic economy, where a customary working environment is expected.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".