The Changing Face of Work in the Context Immigration: Contesting Work Experience on Immigrants in Canada
Bibliographic record
Abstract
Canada’s pursuit of a knowledge-based economy shifted focus of its immigration selection practices to education and skills, favouring economic immigrants to augment existing human capital. Despite their invaluable skills, immigrants are stigmatized as social and political constructions, with skin colour as the basis for social marking. Intersectionality illustrates contemporary configurations of global capital that fuel and sustain growing social inequalities, fostering a rethinking of how experiences can be embedded and shaped by the social categories of gender, race, and class. Therefore, this critical literature review applies intersectionality theory to the changing nature of work experiences of immigrant workers. This research reveals a need for an intersectional inclusive space wherein social inequality can be dynamically re-recognized and negotiated so immigrants can achieve social equality at the individual, community, and social level.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.048 | 0.016 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".