Bibliographic record
Abstract
Precarious employment is on the rise in Canada, increasing by nearly 50% in the last two decades. However, little is known about the mechanisms by which it can impact upon geographical mobility. Employment-related geographical mobility refers to mobility to, from and between workplaces, as well as mobility as part of work. We report on a qualitative study conducted among 27 immigrant men and women in Toronto that investigates the relationship between precarious employment and daily commutes while exploring the ways in which gender, class and migration structure this relationship. Interview data reveal that participants were largely unable to work where they lived or live where they worked. Their precarious jobs were characterized by conditions that resulted in long, complex, unfamiliar, unsafe and expensive commutes. These commuting difficulties, in turn, resulted in participants having to refuse or quit jobs, including desirable jobs, or being unable to engage in labour market strategies that could improve their employment conditions (e.g. taking courses, volunteering, etc.). Participants’ commuting difficulties were amplified by the delays, infrequency, unavailability and high cost of public transportation. These dynamics disproportionately and/or differentially impacted certain groups of workers. Precarious work has led to workers having to absorb an ever-growing share of the costs associated with their employment, underscored in our study as time, effort and money spent travelling to and from work. We discuss the forces that underlie the spatial patterning of work and workers in Toronto, namely the growing income gap and the increased polarization among neighbourhoods that has resulted in low-income immigrants increasingly moving from the centre to the edges of the city. We propose policy recommendations for public transportation, employment, housing and child care that can help alleviate some of the difficulties described.
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 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 | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".