Participatory evaluation of collaborative and integrated water management: Insights from the field
Bibliographic record
Abstract
The Maitland Watershed Partnerships (MWPs) is a multi-stakeholder forum established in 1999 in an agricultural watershed in Southwestern Ontario, Canada. This paper presents 10 lessons emerging from the participatory evaluation of the MWPs carried out in 2005. As suggested in the literature and highlighted by the experience of the MWPs, multi-stakeholder collaboration and integration is about learning how to cope with and take advantage from difference, diversity and divergence. Watershed partnerships are arenas in which different types of knowledges, diverse values and divergent sectoral perspectives, are confronted. In this context, inter-organizational leadership is essential to develop and sustain collaborative advantage among multiple public, private and civil society actors. According to the experience of the MWPs, however, embracing difference, diversity and divergence should go well beyond initial planning stages. Instead, pursuing compromise and agreement should also be at the forefront during the monitoring and evaluation stages. Negotiating indicators for monitoring and evaluation that can address water management both as a social process and a technical process is critical, as is making the distinction between partnership outputs and partnership outcomes.
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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 | 0.128 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".