Immigrants, Communities and Urban Planning [English version]
Bibliographic record
Abstract
Immigrant settlement has never been a static process. Its multifarious patterns keep shaping our communities. Urban planning, as a profession to regulate urban development, should be more aware of the spatial manifestation of immigrant communities and their activities on urban landscapes, and respond to the changes in a more proactive way. In Canadian planning literature, how to manage multicultural issues in cities has drawn increasing attention in recent years. It offers a wide spectrum of focuses, ranging from municipal organizational and legislative responses to immigrant settlement in a broader sense, to human rights and citizenship arguments embedded in planning policies and programs, and to empirical studies of such issues as housing, ethnic retail, places of worship, and neighbourhood design. However, despite the on-going debates regarding immigrant settlement and multicultural planning, there is still no fixed solution that can be drawn upon in response to the increasing ethno-cultural diversity of our society. The current status of multicultural planning is still operating on a case-by-case basis where planners play a reactive role. This is attributable to both the complex nature of immigrant settlement and the unreadiness of the planning system to deal with the challenge. To provide a pluralistic vision for a multicultural city and its people largely depends on whether planning legitimacy is well equipped with the diversity-oriented focus, whether planning practitioners are sensitive to the multicultural reality, and whether the policymaking and implementation process is flexible and culturally respectful.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.211 | 0.050 |
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".