Geographies of Employment among Chinese High-Tech Immigrants in Canada: An Ottawa-Gatineau case study
Bibliographic record
Abstract
For a number of years, Canadian immigration selection policy has deliberately emphasized the human capital characteristics of applicants in determining admissibility for permanent residence. Largely due to these measures, Chinese immigrants today are overwhelmingly well-educated and skilled. This thesis examines the role of geography in shaping Chinese newcomers’ post-arrival employment status, with an emphasis on working in the high-tech sector. Given that Ottawa is a leading node of high-tech employment in Canada, this project initially investigates the probability that Chinese newcomers will work in the high-tech sector in Ottawa-Gatineau relative to other cities. The project subsequently examines the degree to which employment in the high-tech sector in Ottawa-Gatineau is related to ethnic, social and demographic characteristics of local spaces where people live and work. All aspects of the study adopt a gender lens with respect to interpreting employment status. The study finds that Chinese immigrants in Ottawa-Gatineau are more likely to work in this sector than their counterparts in Vancouver and Toronto. They are also more likely to work in high-tech relative to individuals in other immigrant groups or the Canadian-born population. With respect to co-ethnic residential and work spatial configurations, as well as social and demographic characteristics of residential neighbourhoods, the study finds that these factors exert quite different influences on the likelihood that Chinese women and men will work in Ottawa-Gatineau’s high-tech sector. The results are quite distinctly different for women and men, and underline the importance of a gendered analysis of relationships between geographic location/place and employment status.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".