The challenge of engaging ethno-cultural and immigrant residents in the development of urban sustainability policies - the cases of Brampton, Ontario and Surrey, BC
Bibliographic record
Abstract
In an era where the levels of immigration are changing the size and the context of municipal populations throughout Canada, immigrant rich municipalities are forced to find ways to ensure that all voices are heard, and are part of the urban sustainable land-use policy development process. I have chosen to conduct a comparative case study of the municipalities of Brampton, Ontario and Surrey, BC, to discover how they have managed to engage the voices of their ethno-cultural and immigrant populations in their sustainable policy development processes. In order to answer the research questions posed I bring together the theories of “just sustainability” and municipal readiness/responsiveness and have developed a checklist to provide a set of criteria that will allow me to systematically examine the extent to which Brampton and Surrey have been inclusive of their ethno-cultural and immigrant residents.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.044 | 0.020 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".