Social Learning Resulting from Three Consecutive Flood Events in Yorkton, Saskatchewan, Canada
Bibliographic record
Abstract
This paper explores the social learning, and its drivers and outcomes, in Yorkton, Saskatchewan, Canada, following flooding events that occurred in 2010, 2014, and 2016. The data for this study came from 15 semi-structured interviews and 110 newspaper articles concerning the flood events and infrastructure upgrades. Research demonstrates that the flood experience and the interactions and communications between the City, Council, and the public have produced social learning. However, this learning has been single- and double-loop learning. While the data revealed no explicit barriers to social learning, the perception that the public cannot contribute to stormwater management issues may have inhibited the degree of social learning that was achieved. As a result of social learning, Yorkton is now more prepared to deal with future flood events, both in terms of prevention and emergency response. However, social learning is diminishing as a result of the passage of time and the false sense of safety that the infrastructure upgrades create. Diminishing social learning has policy implications for Yorkton as the city has not yet implemented all the proposed flood upgrades.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".