Collaboration and governance : directions for planning in the Manitoba Capital Region
Bibliographic record
Abstract
Regional planning practice often incorporates the concept of govern ance at the cityregion level, in order to avoid adding another level of government.Governance is the practice of various agencies working together to achieve common pulposes.Within this definition, and outside the planning literature, the concept of governance is equivalent to inter-agency collaboration.The purpose of this research is to examine the challenges and benefits of collaborative relationships in the context of the Manitoba Capital Region.Stakeholders are interviewed to provide insight into the cunent relationships within the Region, as well as to provide insight into the development of new relationships.The creation of an inventory of inter-municipal relationships contributes to the development of regional initiatives at the provincial and municipal levels.Inter-agency collaboration fiameworks from the social sciences field are relied upon to characteúze the identified relationships.The Interjurisdictional Cooperation framework and 3 Models of Collaboration are applied to the existing relationships in the Region.Four prominent themes are identified and analyzed including: resources, power differentials, expected outcomes and contextual factors.This study serues as a linkage between collaborative plaming literature and social science literature related to inter-agency collaboration through the description of the specific requirements for collaboration and the applicability to the regional planning process in the Manitoba Capital Region.ACKNOWLEDGMENTS There were several individuals and organizations that contributed to the completion of this research through their participation in the study.I am grateful to everyone who participated in the interviews and I appreciate the candour that they exhibited.The advancement of this research would not have been possible without their willingness to share experiences, both positive and negative.They have expressed countless insights that I will continue to learn from throughout my career.I extend my sincere thanks to my advisor, Dr. Ian Skelton, who provided the immeasurable support, including the much-needed humour, that guided me throughout this process.I am also grateful to rny intemal and external
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".