Enabling community energy planning? Polycentricity, governance frameworks, and community energy planning in Canada
Bibliographic record
Abstract
Cet article examine les expériences de planification énergétique communautaire (PEC) dans trois provinces canadiennes: la Colombie-Britannique; l’Ontario; et la Nouvelle-Écosse. Les études de cas sont utilisées pour répondre à deux questions: dans quelle mesure les expériences du PEC au Canada reflètent-elles des modèles de gouvernance conventionnels dans lesquels l'autorité est partagée entre différents paliers de gouvernement par rapport à des approches proprement polycentriques; et comment de telles activités polycentriques, autonomes et auto-organisées peuvent-elles réussir sans règles globales favorables venant des plus hauts paliers de gouvernements? Les trois cas démontrent des aspects de la gouvernance polycentrique, toutefois ils les cas mettent en évidence les limites des initiatives de gouvernance polycentrique locale en l'absence de politiques stables et cohérentes venant de paliers supérieurs. Ces résultats soulèvent des implications importantes sur la capacité des initiatives communautaires à fournir des réponses efficaces à des défis mondiaux complexes tels que le changement climatique en l'absence de cadres politiques et de gouvernance globaux favorables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".