First Nation capacity in Quebec to practice integrated water resource management
Bibliographic record
Abstract
The emergence of Integrated Water Resource Management (IWRM) coincides with the growth of watershed associations in Québec. As a collective entity of stakeholders, these watershed associations use collaborative efforts to achieve IWRM. First Nations are often cited as priority stakeholders. Despite this 'priority' recognition, First Nations are rarely present in this new paradigm shift in water management. This is the case in Québec's Outaouais and Chateauguay watersheds. However, identifying First Nation capacity strengths and limitations provides a greater understanding as to their absence from IWRM participation. First Nation capacity to practice IWRM requires greater research. The purpose of this study is to apply an analytical framework to assess the overall capacity of two First Nation communities to practice IWRM in the province of Québec. The capacities of Kitigan Zibi and Kahnawà:ke First Nations were evaluated with respect to actor networks, information management, human resources, and technical, financial, and institutional dimensions. This study recommends that future Québec IWRM initiatives with First Nation collaboration need to be directed towards strengthening actor networks capacity and understanding the complexity of First Nation perspectives. In addition, study results indicate First Nations with limited financial capacity will experience reduced actor networks, information management, human resources, and technical capacity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".