Consuming the neighbourhood? Temporary highly skilled migrants in Montreal’s Mile End
Bibliographic record
Abstract
The neighbourhood of Mile End, in Montreal, Canada, is known to be a transient, multicultural hub, home to successive waves of migrants. It is internationally acclaimed in popular travel blogs and world lists of ‘hip’ neighbourhoods on the basis of indicators such as number of artists per square kilometre, café culture, and established independent shops. Yet a core of socially involved and rooted residents nurture the village-like vibe of the area. In the past decades, the eastern industrial zone of Mile End has seen a concentration of IT, gaming and film industries rise considerably, especially with the arrival of a large multinational company that brought a significant demographic of mostly affluent professional 20- and 30-something males. Local companies are recruiting temporary high-skilled migrants to respond to a growing demand for various types of designers and programmers. This paper documents the localised everyday life experience of temporary professional migrants’ in the neighbourhood. It considers migrants’ influence on the revitalisation of the area, and their the impact on the urban culture. Ethnographic data on how temporary migrants’ struggle for integration into forms of civic engagement are juxtaposed against their transnational urban lifestyles mostly defined by modes of consumption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".