Road to nowhere or to somewhere? Migrant pathways in platform work in Canada
Bibliographic record
Abstract
Canada boasts some of the most highly educated migrants in the world, but it is well recognised that these migrants face many labour market barriers to gainful employment despite their experience and qualification. Administrative data indicate that the proportion of gig workers is considerably higher among migrants, yet little is known about the various perceived and desired pathways of migrants who choose to pursue platform work. In this inductive, qualitative study, we interviewed 35 platform workers in Canada regarding why and how they turned to such forms of work and how it fits their overall plans for integrating into the Canadian labour market. Adopting a grounded theory approach, we found six pathways into platform work ranging from those who feel in control of the situation as a means to an end, to those who feel trapped in it, unable to find alternatives. We question how these pathways relate to macro factors (e.g. immigration status, professional status), meso factors (e.g. education and skills, networks) or micro factors (e.g. stage in life cycle, aspirations). In our analysis, we consider the critical insights offered by scholars on racial and platform capitalism in understanding the factors impacting migrants’ pathways into platform work in Canada. Our findings suggest that these structural inequalities are further perpetuated within platform work, even though in theory Canada's immigration system is merit-based with emphasis on high human capital. Migrants’ engagement in platform work is a piece of a larger puzzle of segmented labour markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".