Cosmopolitanism at the Local Level: The Development of Transnational Neighbourhoods
Bibliographic record
Abstract
Abstract To set the scene for this chapter, I would like to begin at the micro-geography scale of the street I live on in Vancouver’s Eastside (or, more precisely, the lane behind my street). My street is situated in a neighbourhood called Cedar Cottage, which for around 100 years has been an area of active immigrant reception, at first of new arrivals from the United Kingdom, then central, southern and Eastern Europe and, most recently, the Asian side of the Pacific Rim. According to the la est census (1996), 72 per cent of the residents of my part of Cedar Cottage are immigrants, 20 per cent having arrived in the last ten years. A large variety of national backgrounds are represented, including remnants of the earlier European migrations and, of course, many Asian-Canadians. In fact, just below 60 per cent are classified by the census as ‘visible minorities’, meaning that they are of non-Aboriginal, non-European descent. Of these, the bulk is of Chinese origin, from a number of countries that include China, Hong Kong, Singapore, Taiwan and Indonesia. Beyond the Chinese-Canadian population, there are still many, mainly older, Europeans, and new immigrants from various countries.
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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 | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".