The impact of local government action on climate change : The City of Athens and the Town of Tecumseh
Bibliographic record
Abstract
Climate change is undoubtedly one of the biggest environmental threats of the twenty-first century. Recent developments have shown that we are highly vulnerable to climate change. Climate change is predicted to have harmful, and permanent consequences on the planet and the entire environment. Yet federal and national governments around the world are struggling to formulate policy initiatives to slow down or become resilient against the changes in the climate. The research goal of this paper is to understand the impact of local governments/municipalities on climate change and why their inclusion in the conversation on climate change response is important. Local government control of areas that include energy-use, land-use planning, waste and wastewater, disaster response, and transport makes them well-positioned to tackle climate change.\nThe impact of municipal governments on climate change are examined through US and Canadian case studies. Specifically, the paper examines the City of Athens, Ohio and the Town of Tecumseh, Ontario. The size, geography, population and the federal structure of the City of Athens and the Town of Tecumseh makes them comparable case studies for this study. The emergence of voluntary official plans by both municipalities despite the lack of guidance from federal and national governments demonstrate that local governments can be relied upon as an integral player in tackling climate change. This paper finds that local governments should be included in a multi-level approach to climate change to force more local progress and influence future action on regional, state, and national climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".