Metropolitanization and the Restructuring of Urban Governance in Canadian Metropolitan Areas
Bibliographic record
Abstract
The restructuring of urban economies and welfare states of the developed world over the last twenty years or so, often as part of a broad neoliberal agenda, presents challenges to the delivery of quality municipal services. With reductions in government spending and program breadth at national levels in many countries, governments at the sub-national and local levels have had to take on increased responsibilities. At the same time, local governments and metropolitan areas have often been compelled to become more entrepreneurial and competitive, both indirectly via the compulsion of new regulatory regimes and a restructuring global economy, and directly through the imposition of new structures and financing arrangements on the part of upper-level governments onto municipal governments. This may produce greater unevenness in the capacities of local governments to maintain local services, and to meet the needs of resident low-income populations. Without offsetting transfers, positive feedback relationships develop between concentrations of low-income populations and municipal budgetary difficulties, which then segregate the poor in areas with limited services but higher taxes, while wealthier residents benefit from residence in low-tax, high-service jurisdictions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".