Bibliographic record
Abstract
With the demise of the Old Regionalist project of achieving good regional governance through amalgamation, voluntary collaboration has become the modus operandi of a large number of North American metropolitan regions. Although many researchers have become interested in regional collaboration and its determinants, few have specifically studied its outcomes. This book contributes to filling this gap by critically re-evaluating the fundamental premise of the New Regionalism, which is that regional problems can be solved without regional/higher government. In particular, this research asks: to what extent does regional collaboration have a significant independent influence on the determinants of regional resilience? Using a comparative (Canada-U.S.) mixed-method approach, with detailed case studies of the San Francisco Bay Area, the Greater Montreal and trans-national Niagara-Buffalo regions, the book examines the direct and indirect impacts of inter-local collaboration on policy and policy outcomes at the regional and State/Provincial levels. The book research concentrates on the effects of bottom-up, state-mandated and functional collaboration and the moderating role of regional awareness, higher governmental initiative and civic capital on three outcomes: environmental preservation, socio-economic integration and economic competitiveness. In short, the book seeks to highlight those conditions that favor collaboration and might help avoid the collaborative trap of collaboration for its own sake. More specifically, this research concentrates on the effect of bottom-up, state-mandated and functional collaboration, the moderating role of regional awareness, governmental initiative and civic capital on environmental preservation, socio-economic integration and economic competitiveness. In short, the book seeks to understand whether and how urban regional collaboration contributes to regional resilience.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".