Interlocking Lives: Employment Mobility and Family Fixity in Three Gentrifying Neighbourhoods of Montreal
Bibliographic record
Abstract
Abstract This article explores the relationship between employment mobility, family fixity, and gentrification in the lives of 36 residents in and extended commuters to Montreal's southwest borough. Once described as the birthplace of industry in Canada, the neighbourhoods of Saint‐Henri, Little Burgundy and Point Saint‐Charles have undergone sweeping changes in recent decades. Inner‐city areas are not necessarily where one expects to find mobile workers, but this is changing due to shifting gender roles, the rise of dual‐income households and gentrification. Michael Savage's concept of ‘elective belonging’ proved particularly useful in understanding this connection. With its proximity to childcare, schools, stores and workplaces, the central city permits a more equitable division of labour within the household. Our place‐based approach to mobile work enables us to capture a wide spectrum of experience, ranging from people with extended daily commutes to those whose work takes them away from home for days, weeks or months at a time. Our interviews reveal a connection between employment mobility and family gentrification, as upwardly mobile families find ways to localize other aspects of their lives. The simultaneity of mobility and immobility are often essential, especially in dual‐income households. One parent's mobility often leads to the relative immobility of other family members.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".