The problem(s) with cities: Analyzing the need for greater municipal empowerment in Canada
Bibliographic record
Abstract
Canada’s big cities face big, complex problems that undeniably hamper their ability to govern effectively. In addition to the fact that they are highly diverse and culturally distinct from their surrounding regions, many structural issues exist within the architecture of big city governments that make the act of governing extremely difficult, and the result is an arguably poorer quality of life for urban citizens. Not only are urbanites underrepresented in the federal system, meaning that the issues they face in their day-to-day lives are less discussed in Ottawa, but urban-based minorities, including racialized and poor Canadians, are further marginalized. Municipalities, presided over by provincial governments, have little control and minimal ability to address the needs of their citizens in serious and thoughtful ways: this is not only harder on the administrators of big cities, who feel significant frustration at their own impotence, but is also hard on citizens who are unable to hold their city governments accountable for anything, including the bad behaviour that sometimes results (Slayton 2015, 1-2).[...]
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".