The System of Local Government in Canada: An Introduction
Bibliographic record
Abstract
This entry has been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay”. LoGov aims to provide solutions for local governments that address the fundamental challenges resulting from urbanisation. To address this complex issue, 18 partners from 17 countries and six continents share their expertise and knowledge in the realms of public law, political science, and public administration. LoGov identifies, evaluates, compares, and shares innovative practices that cope with the impact of changing urban-rural relations in five major local government areas: (1) local responsibilities and public services, (2) local financial arrangements, (3) structure of local government, (4) intergovernmental relations of local governments, and (5) people’s participation in local decision-making. The present entry represents the general introduction of the LoGov Report on Canada providing an overview to the system of local government in the country. To access the full version of the report on Canada, the various practices in the five above-mentioned areas of interest, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.025 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".