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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".