Action research to improve water quality in Canada<b>’</b>s Rideau Canal: how do local groups reshape environmental governance?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The historic Rideau Canal, spanning 200 km between the Canadian cities of Ottawa and Kingston, is a world heritage site and recreational waterway. The waterway presents a governance challenge, with multiple jurisdictions and agencies responsible for its management, making it difficult to establish a common vision to address environmental issues. Local stakeholders are concerned about toxic algal blooms in the downstream section of the Canal (the Lower Cataraqui region) because these blooms limit use of the system and pose a potential threat to human and environmental health. In the absence of a strategy to effectively manage water quality, a grassroots group called the Three Lakes Water Quality Group (TLG), has brought various stakeholders together to initiate transdisciplinary discussions and find solutions. This article presents findings from action research with the TLG. Specifically, it examines (1) the activities and concerns of the TLG in the governance arena, (2) the views of local stakeholders on social-ecological issues, (3) the potential of using collaborative systems thinking to capitalise on the TLG’s activities. Our analysis is informed by interviews and a workshop. We recommend that the TLG mobilise collaborative systems thinking when meeting with other stakeholders to discuss raising awareness, enforcing policy and producing knowledge about water quality issues in the region. These findings have implications for the entire Rideau Canal and other historic waterways by revealing the potential of local residents to initiate dialogue and drive future co-governance efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 it