Greener Small Cities: Deploy Environmental Action Faster and Smarter
Bibliographic record
Abstract
How might we support small Canadian cities (1k-100k) to become greener and smarter in a way that prioritizes deliberate and proportional action whilst accounting for their unique composition? \n \nGreener Small Cities: Deploy Environmental Action Faster and Smarter, was built to support those in charge of climate action at the municipal level of small Canadian cities with strategy, systemic, and foresight tools. The first half of this report explores the theory surrounding environmental action and smart technologies within a Canadian context. Action is the focus of the second section. We’ll walk readers through the steps of building a comprehensive, measurable, equitable, and effective environmental action plan of their own, or revamp the one they currently have in place by learning how to include and manage stakeholders, decide on a vision, set strategic pillars, strategies, and strategic actions, envision future states, leverage data, secure funding, and much more.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".