How Depth of Social Learning Affects Post-flood Depth of Adaptation
Bibliographic record
Abstract
Extreme flooding events in Canada are increasing both in their frequency and repeated occurrence in the same geographical location. This dissertation examines the cases of Fort McMurray, AB and Fraser River Basin, BC to examine what influences social learning depth and how does this affect post-flood policy change in Canada. The research filled some notable gaps in the literature, including consideration of power in policy making, provided both theoretical and empirical research, contributed to the research on the limited adaptation and learning literature in Canada, and used the Multiple Streams Framework with a Political Ecology lens. Using Interpretive Analysis as a research approach, semi-structured interviews and document analysis were used, emphasizing the importance of local knowledge and framing. This research suggests that beliefs, collaboration and governance, resources, and politics are determinants to depth of social learning and impact depth of adaptation and policy change post-flood. Further, MSF can provide a means to explore complex cases and the use of PE can bring forward nuances of power that may be missing from the policy change literature. Findings suggest that there are nuances in these cases that can provide insight into improved future policy development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".