The River Runs Through It: The Case for Collaborative Governance in the National Capital Region
Bibliographic record
Abstract
Canada's National Capital Region (NCR) is unique inthat it is comprised of two neighboring cities, Ottawa and Gatineau, which liein two separate provinces separated by the Ottawa River.This studyexamines the role of city-regions in the world and analyzes the influence ofpolitical and socio-economic similarities and dissimilarities on thefunctioning of the NCR. Initially, past literature regarding the impact of local governance and ofcollective action on city-regions is presented.Then, the complicationsand special circumstances attributed to border and boundary issues arediscussed. Following the discussions of previous literature, somebackground information about Ottawa and Gatineau is provided.Thehistorical and political differences and similarities of both cities arediscussed, as are the economic infrastructure and the human capital of eachcity.Within each city lie cultural and linguistic differences, whichfurther impact upon the collaborative abilities of both cities.Thecomplications resulting from a city-region's desire to be economicallyinterdependent while politically independent are examined. Among high-technology firms, the advantages of city-regional governance arebecoming more apparent and are outlined according to those advantages thatexist in the NCR.Recommendations for strengthening city-regions arediscussed. (AKP)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.027 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".