Learning as sociocultural practice: Chinese immigrant professionals negotiating differences and identities in the Canadian labour market
Bibliographic record
Abstract
The last few decades have witnessed both an expansion and a transformation of immigration flows, which pose significant challenges with respect to how people work with differences across culture and space. Against this background, this paper explores how some Chinese immigrant engineers respond to differences in the Canadian labour market. It not only examines some of the learning practices engaged by the immigrants as they negotiate professional niches and professional identities, but also demonstrates how their learning process is socially mediated. In particular, it shows that licensure processes and immigrant settlement services are instrumental in entering immigrants into the cultural and social order in the Canadian labour market. It pinpoints a lack of recognitive justice in the ways in which immigrants' learning processes are institutionally reshaped. Informed by the sociocultural approach, this paper treats learning as a social participatory process, through which individual identities are constituted and reconstituted.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.025 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".