Multi-level Governance: Getting the Job Done and Respecting Community Difference – Three Winnipeg Cases
Bibliographic record
Abstract
Multi-level governance is seen by different commentators as addressing a varied array of concerns. Some see it as a means of fulfilling the norms of the new public management, and thereby of freeing the administration of government programs from the constraints imposed by centralized bureaucracy. Some assess it in terms of dealing with policy problems so complex that they can only be addressed by concerted and co-ordinated efforts of more than one level of government and, often, a variety of agencies. At the same time, multi-level governance is also associated with the attempt to introduce a greater degree of flexibility into federal policy-making, in order to ensure that federal policies respect the unique characteristics of different communities. In this study, we bring all of these concerns to bear on three case studies of the multi-level governance of federal properties in Winnipeg, the James A. Richardson International Airport, the Kapyong Barracks and The Forks. The three properties are all administered by agencies at least one step removed from direct government supervision. We posed two research questions: 1) Are the operations of these agencies, and the character of their relations with federal and municipal governments, appropriate to the ends they are meant to serve? 2) Do they respect community difference? In all three cases, we find that the objective of effective management is reasonably or very well served, but respect for community difference is much less evident.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".