Road to nowhere or to somewhere? Migrant pathways in platform work in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 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 | 0.000 |
| 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 it