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Record W3214448803 · doi:10.32920/ryerson.14656890.v1

Community energy planning in Ontario and British Columbia: Climate action at a municipal planning level

2021· preprint· en· W3214448803 on OpenAlexaffabout
Jessica Brodeur

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnergy planningCommunity planningEnvironmental planningEnergy (signal processing)Action (physics)BusinessProcess (computing)Energy policySubject matterEnvironmental resource managementPolitical scienceComprehensive planningClimate changeLocal communityPublic administrationGeographyEnvironmental scienceRenewable energyEngineeringComputer science

Abstract

fetched live from OpenAlex

Community Energy Planning (CEP) is a process that allows municipalities to develop and implement local climate action, meet carbon reduction goals, and ensure a steady supply of clean energy. This MRP compares CEP in four municipalities in Ontario and British Columbia, to examine the reasons that led municipalities to undertake CEP and the roles that the municipalities undertook in the process. By using a policy comparison and interviews with Subject Matter Experts, the role that the municipality played to develop and implement CEP, and the role of the CEP within the community were evaluated. The municipalities studied were seen to have undertaken the expected roles to varying degrees and with various methods. Key Words: An article on energy and emissions planning in Canadian municipalities, used the key words: community energy planning, climate action, energy policy, local environmental planning

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0170.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.127
GPT teacher head0.338
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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