Advancing metro-regional governance : exploring fiscal regionalism via a regional asset district for the Manitoba capital region
Bibliographic record
Abstract
The proportion of the Manitoba Capital Region (MCR) population living in the capital city, the City of Winnipeg, is steadily declining.Municipalities are competing against each other, and against city-regions around the globe, for new development and the associated tax revenue.Further, municipalities are facing staggering infrastructure debts and are searching for new sources ofrevenue.Regional governance represents a method of addressing these challenges and improving the quality of life for all citizens in a city-region.In particular, fiscal regionalism utilizes regional fiscal mechanisms such as tax (revenue) sharing while not threatening municipal autonomy, yet also respecting the existing configuration and boundaries of municipal governments.This practicum examines a specific form of fiscal regionalism, the regional asset district (RAD), and explores the applicability of the concept to the MCR.As well, this practicum examines the state of regional governance in the MCR in part through key informant interviews.City-regions with regional asset districts are researched to determine their formal and informal governance structure.The regional asset districts are studied as to how they were established and their operation.The research indicates that MCR relations are improving but civic regionalism and regional planning are not at a level to support a RAD.However, the benefits of a RAD are great enough to recommend both more research, and that the region work together, towards establishing a RAD as a long-range goal.If pursued, vertical tax sharing of the Provincial Sales Tax is recommended to fund, initially, a voluntary RAD comprising cultural and/or environmental assets.ACKNO\ilLEDGMENTS I especially wish to thank the key informants who participated in this research and for being so generous with their time.I am grateful for the guidance, feedback, and patience I have received from my advisor, Dr. Ian V/ight.I am also grateftrl to have had the benefit of the expert knowledge and wisdom of the other members of my committee
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".