The “right‐to‐farm” in Lac Saint‐Pierre (Québec, Canada) floodplains: Are problem‐framing processes able to foster conservation conflict resolution?
Bibliographic record
Abstract
Abstract Using qualitative data, we investigate the impact of the problem‐framing process on stakeholder mobilization for fish habitat restoration and its influence on transforming agricultural practices in floodplains. Problem‐framing involves defining and delineating a problem to suggest practical and measurable solutions for addressing it. We are examining how the conservation conflict changes over time in Lac Saint‐Pierre (LSP), part of the St. Lawrence River Basin in Québec, Canada. Such conflicts arise when there are differing perspectives, interests, or actions regarding conservation goals and objectives. In recent decades, the LSP floodplain has undergone significant changes, particularly the conversion of perennial crops to intensive annual crops, which are deemed incompatible with the ecological needs of yellow perch. This species has experienced a notable decline in LSP since the 1990s, prompting Québec authorities to impose a moratorium on yellow perch fishing in 2012 to safeguard stocks. This moratorium has catalyzed efforts at the policy level to restore its habitat. However, it has also engendered tensions between agricultural activities and conservation endeavors aimed at restoring yellow perch habitat, constituting the conservation conflict under investigation. To investigate this issue, we adopt a post‐normal science approach characterized by reflexivity, inclusivity, and transparency in addressing epistemological and ontological uncertainties among LSP stakeholders. Our findings offer insights into stakeholders' perspectives on the problem‐framing process and its outcomes, highlighting both supportive actions enhancing the effectiveness of certain strategies among LSP stakeholders and barriers hindering their mobilization. These results underscore the importance of incorporating diverse stakeholder perspectives during the problem‐framing process to enhance the robustness of the science–policy interface.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".