SPATIAL SHIFTS, WORKER RIFTS: LABOUR CONTROL AND RESISTANCE AMONG MIGRANT REMOTE WORKERS IN NORTHEASTERN ONTARIO AND THE MARITIMES
Bibliographic record
Abstract
The rise of remote work during and after the COVID-19 pandemic drove many workers to migrate from large metropolitan areas to smaller, more rural regions, enabled by the detachment of work from the physical office. Using interview data from 27 Canadian remote workers who migrated to northeastern Ontario or the Maritimes between 2020-2024, this study contributes to the larger body of remote work literature by investigating how working remotely, and at a distance, affects labour control and resistance dynamics. This thesis contributes a worker-centered perspective to remote work literature, which is often shaped by a productivity bias rooted in employer-focused fields. It also fills key gaps by exploring effects of internal migration on control and resistance among white-collar, non-unionized remote workers—topics that remain largely underexamined. Data from participant interviews yielded several key insights about migrant remote workers. They often use spatial distance as a tool to avoid employer control. However, their employers also utilize remote workplace technologies to increase surveillance and exert casual control from afar. Many migrant remote workers also indicate that when their spatial autonomy is threatened, they may respond with acts of individual or collective resistance. Finally, while many workers describe feeling highly productive and “in control” of their work, these accounts reveal a fallacy in perceived control, as this sense of autonomy often masks deeper forms of employer oversight.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".