Making the Case for Coastal Buyouts in Canada
Bibliographic record
Abstract
This report presents findings from a research project investigating the potential for buyouts as a strategy to adapt to natural hazards and climate change processes in coastal communities of Canada. The central research question the project sought to address is: How the case can be made for coastal buyouts as an adaptation strategy for natural hazards and climate change in Canada? To produce practical and relevant findings from the project, this was complemented with a secondary purpose: to produce recommendations for Canadian communities to begin looking at buyouts as an adaptation strategy. Literature review was undertaken to gain a sense of broader themes affecting the existing scape of hazard management and climate change adaptation in Canada. Through this, three key themes emerged: the traditional protectionist approach to hazard adaptation; the problem of ongoing development of vulnerable lands; and, the potential for buyouts to foster ecosystem services along the coast. Case studies of the states of New Jersey and New York were completed to gather a sense of the framework for implementing buyouts. These provided insights into how buyout programs have been implemented in practice, including some successes and gaps that can be used to inform buyout program structuring in Canada. Finally, informant interviews offered the opportunity to draw on the literature review and case study findings and to find ways of applying them to a Canadian context. These interviews highlighted barriers, opportunities, and needs for implementing buyouts in Canadian communities, through discussions informed by literature review themes and case studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".