What are the real drivers behind river restoration projects?
Bibliographic record
Abstract
Integrating hydrogeomorphological principles into the restoration of degraded rivers can achieve sustainable results for a variety of objectives and human benefits that are consistent with the potential functioning of rivers as well as their uses. Despite recent management approaches inspired by these principles, several restoration projects largely involve controlling river processes and target specific one-dimensional objectives often associated with the habitat of a few high-valued fish species or with rivers steadiness and aesthetics. Although there is overall a lack of post-project monitoring, several projects appear to have failed or had mixed success. This research aims to answer the question: What are the real drivers behind river restoration projects? Four restoration projects in Quebec (Canada) were characterized through a qualitative research process of support and interviews with the organizations running them as well as with two representatives of ministries involved in river restoration and management. The results identify two major drivers for the formulation of restoration objectives: project funding and stakeholder expertise. We propose a new analytical framework including these drivers, which appear to act as both conditions and motivations guiding the objectives of the projects and their diversity. Following diverse sociocultural and legislative contexts shaping these drivers, they may facilitate or restrict the integration of hydrogeomorphological principles towards diversified objectives and benefits. This supports regulation that is better informed by scientific knowledge about hydrogeomorphological and sociocultural river dynamics, knowledge sharing between academic researchers and environmental organizations, and collaboration between stakeholders and communities living around rivers.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".