It Takes a Region: A Case Study of Growth and Governance in the St. John’s city-region of Newfoundland and Labrador
Bibliographic record
Abstract
This study is about understanding a less typical Canadian response to metropolitan regional governance in the St. John’s city-region of Newfoundland and Labrador. Governance of city-regions has become a prominent concern of urbanizing areas around the globe, yet the political dynamics of the local context significantly impact adoption of regional solutions to this challenge. In this research, content analysis of policy reports and consulting studies were combined with interviews of provincial and municipal leaders, planners and regional organizations. The study found that despite a number of operationally effective single-purpose regional bodies there is a high level of power imbalance, distrust of the centre city, and a history of relations that are not conducive to advancing regionalism. Still, there are ongoing forums that continue to advance the region as a legitimate scale for action and participants see value in the regional approach. This study concludes that Provincial intervention is necessary to steer the leadership of the region toward workable regional solutions. In order to enhance inter-municipal collaboration in regional governance the Province needs to act as a facilitator to move beyond historical power dynamics and build trust. Furthermore, in order to improve relations with its neighbours, the City of St. John’s has to seek collaborative solutions and put the amalgamation ghost to rest.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".