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Record W4416926496 · doi:10.36939/cjur/vol33no1/art421

Getting the job done: Barriers and enablers to municipal climate action in Greater Victoria

2024· article· W4416926496 on OpenAlexaffvenueabout
Charlotte Masemann, Tamara Krawchenko, Ekaterina Rhodes

Bibliographic record

VenueCanadian journal of urban research · 2024
Typearticle
Language
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGovernment (linguistics)Scope (computer science)Local governmentAction (physics)Climate changeGreenhouse gasPolitics

Abstract

fetched live from OpenAlex

Climate action is high on the agenda for many local governments across Canada and yet greenhouse gas emissions do not decline. The literature on policy implementation points to the importance of the working level in understanding the scope for climate action and the types of barriers that professionals face in advancing climate goals. This study contributes to this literature by exploring the barriers and enablers to municipal climate action through focus groups with municipal staff members across Greater Victoria, British Columbia, in the key sectors of buildings and energy, transportation, and solid waste. Six categories of barriers and enablers are identified: funding, staffing, legislation/regulation, governance, information, and politics, with the first categories representing a form of government capacity. Given the overall lack of power in implementing economic and regulatory policies, municipal government officials emphasize the importance of collaboration, data communication, and political leadership in implementing climate action at the local level.

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)0.005
metaresearch head score (Gemma)0.011
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.005
Scholarly communication0.0050.001
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.069
GPT teacher head0.334
Teacher spread0.266 · 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
Published2024
Admission routes3
Has abstractyes

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