Bolstering Ontario Land-Use Planners’ Adaptive Capacity for Resilient Climate Change Adaptation through Education
Bibliographic record
Abstract
For many land-use planners across the province of Ontario, the region that my research examines, the issue has been raised that the adaptive capacity required to effectively and efficiently implement the climate change adaptation strategies and policies that they have been mandated to employ is lacking. Even though the tools and resources are there in abundance, the ability to implement such strategies and policies has been recognized to depend on land-use planners’ understanding of the climate issue at hand and the number of accessible human and technological resources . This is the central argument of this paper. As such, I use this research opportunity to explore how to bolster the adaptive capacity of Ontario’s land-use planner in these ways for a better response to the challenge that climate warming poses. I begin with a brief history of the climate change regime, along with a brief explanation of the climate science behind the warming. I then proceed to discuss the role land-use planning plays in contributing to climate warming, how it can redirect its efforts to reduce our carbon footprint, the challenges land-use planners face when tasked to implement adaptation strategies and how it can be solved through the bolstering of their adaptive capacity using the resilience framework. This is followed by a discussion on the work that the province of Ontario is doing through the BRACE Project to help bolster the adaptive capacity of the land-use planner. Through this research, my objective is to highlight the gap that currently exists in our adaptation efforts where those we depend on to implement these climate change adaptation strategies are lacking in their ability to carry out the work due to their lack of climate change adaptive capacity and how to bolster this through a resilience framework that presents us with a solution – education.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".