Navigating land use after managed retreat: decisions facing local governments in the post-buyout environment
Bibliographic record
Abstract
Five years following the Quebec Spring 2019 floods in Canada, the City of Gatineau is grappling with questions on how to move forward with municipal land use plans that integrate provincial flood protection policies. This longitudinal study analyses the policy changes in the Province of Quebec’s flood management regime and assesses the post-buyout land use decision making process. Using mixed methods, we explore the evolution of buyout policies, assess the challenges in maintaining both occupied and vacant lots, and document potential post-buyout land use options that would reconcile the benefits of floodplain restoration and provide recreational spaces for the community. The results indicate the institutional alignment of provincial buyout policies and regulatory tools, such as the special intervention zone that support the relocation of residents whilst acknowledging that some areas are no longer viable and prohibiting future redevelopment. This Canadian case study illustrates a municipality that has developed a community master plan though a lack of funding and direction from senior governments continues to hinder the city’s progress indicating that flood risk management is challenging to implement without the adequate coordination of responsibility and elimination of fragmentation between different levels of government.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".