Where strangers become neighbours. The story of the Collingwood Neighbourhood House and the integration of immigrants in Vancouver
Bibliographic record
Abstract
Documentary distributed by the National Film Board of Canada (Montreal, Quebec, Canada) - http://www.mongrel-stories.com/films/where-strangers-become-neighbours/ \n\nABSTRACT\n\nMigration has always been an important feature of human history, but never more so than the past two decades. But what happens when increasing numbers of strangers move into a neighbourhood, bringing with them different histories and cultures, religions and social practices, and often, urgent needs for housing, language training, schools and jobs? How do newcomers, as well as members of the ‘host’ society, develop an everyday capacity to live alongside those perceived as different, strange?\n\nOur story explores this contemporary global social issue by looking at one neighbourhood – Collingwood – in the City of Vancouver. 38% of metropolitan Vancouver, and 51% of the City’s residents are foreign born. Collingwood, a predominantly Anglo-European community until the 1980s, has been transformed since then by the arrival of large numbers of East, South, and South East Asians, Africans, and Latin Americans. A neighborhood that, just 20 years ago was locking its doors, afraid of change, and telling immigrants to go back where they came from, is now a welcoming place for everyone.\n\nHow did this happen? How do strangers become neighbours?
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".