A Fine Balance: Exploring Job Quality in Platform Work Between Migrants and Nonmigrants
Bibliographic record
Abstract
Migrants’ engagement in digital platform work is pervasive in many cities around the world and certainly in Canada's metropoles (Toronto, Vancouver, and Montreal). While highly precarious, platform work has been shown to offer pathways into labor market integration for newly arrived migrants. Based on 62 qualitative interviews with digital platform workers, this article compares the work experiences of newcomers, settled migrants and nonmigrants engaged in platform work in Canada's three largest cities. The study examines how the different stages of their immigration journey shape the ways in which migrants (versus non migrants) perceive and evaluate their engagement in digital platforms. Satisfying urgent needs, achieving stability and allowing for personal development are three key elements that emerge from this study. These findings invite us to consider what are the main elements in current notions of “quality work” among migrants and nonmigrants and to consider how platforms shape broader labor market integration processes
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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".