The Past, Present, and Future of Canadian Cities
Bibliographic record
Abstract
In 1861, just a few years before Confederation, 84 per cent of Canadians lived in rural areas; today, it’s less than 20 per cent. Our municipal governments are asked to do more for their citizens than ever before, yet they must confront myriad challenges – from the public health pandemic to the housing crisis – without the tools they need. They have no constitutional protection from jurisdictional overstepping by provincial governments and no assurance that they will be able to complete any effort they undertake. The Past, Present, and Future of Canadian Cities explores the historical functions of municipalities, their current ability to tackle major problems, and what the future holds for shifting legal and political powers. This volume examines how pre-Confederation cities came to have their current constitutional and legislative forms; how current local governments make decisions within existing legal parameters, highlighting Indigenous-municipal relationships and emergency management; and, finally, looks to the world to investigate future innovation in municipal governance. The Past, Present, and Future of Canadian Cities makes the case that constitutional concepts must be repurposed to support the transition from nation-building to city-building in a global context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".