All Hands On Deck?: The State of Municipal Climate Change Strategizing in Ontario
Bibliographic record
Abstract
The impacts of climate change have become increasingly wide-spread and dire, leading to an intensification of questions around how we can best respond to the climate emergency. Academic literature has identified two broad shifts in responses to climate change. First, cities have increasingly become leaders in climate change strategizing. Additionally, cities have emphasized working with external partners to collaboratively develop and implement their strategies. Second, there have been increasing calls for Indigenous knowledges to inform climate change strategies. These calls partly reflect the holistic nature of Indigenous knowledges and the strengths of strategies informed by them. This dissertation uses a mixed-methods approach to understand how Ontario municipalities have responded to these shifts and evaluates the state of climate change strategizing in Ontario. Using thematic analysis, the dissertation examines data derived from a survey, interviews, and document analysis to understand what kinds of policies Ontario municipalities are implementing, what external partners are being engaged with, and why. Given the key research finding of limited engagement between Ontario municipalities and Indigenous Peoples, the dissertation applies an anti-colonial informed \nintegrative framework for collaborative governance to understand the factors that inhibit and encourage municipal-Indigenous partnerships. The dissertation concludes that a web of system context variables (as defined by the integrative framework) challenge the ability for municipal staff to collaborate with Indigenous Peoples. Additionally, the heavy reliance on frameworks provided by transnational municipal networks (TMNs) impedes early and ongoing partnership between Indigenous Peoples and governments. Finally, the dissertation suggests that Indigenous Peoples may not desire to partner with the governance institutions of settler colonial state. In offering these conclusions, the thesis makes important theoretical, empirical, and practical contributions that may support the future development of more robust responses to 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".