Danger zones and hot spots: Traversing Canadian and Australian narratives of urban multiculturalism
Bibliographic record
Abstract
The ethnicity of urban space has long been an element in the burgeoning discourse of national multiculturalisms; so much so that spatial theorist Edward Soja uses the term “ethni-city” to speak of so-called postmodern or postcolonial urban geographies (239). In our focus on the urban, we point to both the conceptual and material thresholds of multiculturalism within the borders of the city, as well as the internal urban/suburban borders that delineate belonging. These are often as strongly patrolled as larger national borders. In taking up Sneja Gunew’s call in Haunted Nations for comparative and critical work on multiculturalisms, this paper offers preliminary and exploratory avenues and points of departure, and aims to particularise the multicultural as an encounter and experience that is regulated spatially and corporeally.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.053 | 0.033 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".