Centring Water in Impact Assessment: Reconsidering Environmental and Cultural Flows in Development Decision-Making in Canada
Bibliographic record
Abstract
Considering regional impacts on downstream communities and ecosystems is challenging in impact assessments. We suggest that environmental and cultural flows have the potential to be applied to protect water more comprehensively in assessment but are currently underutilized. Environmental and cultural flows refer to adequate water quantity and quality for the environment and Indigenous rights. Through interviews and a scoping review of legislation and assessments, we address how these concepts are and could be embedded within Canadian impact assessments. To date, environmental flows have been considered in assessments involving dams, oil and gas, and mining, and the focus has been on fish and habitat, rather than Indigenous rights and cumulative withdrawals. We propose Regional Readiness through water councils, change, and consensus (three Cs) to prepare watershed actors to protect environment and cultural flows in impact assessments. The three Cs are: (1) Advisory councils dedicated to creating regional objectives and rules for ecosystem and rights-based needs, (2) Assessing hydrologic and water quality change with regional data and relationships to water, and (3) Building consensus on the cultural and ecological significance and sensitivity of water bodies. Development of this framework follows examples from Canadian water-related assessments and initiatives: Wolastoq Ecological Limits of Hydrological Alteration, Athabasca Chipewyan and Mikisew Cree assessments, Yinka Dene Water Management Policy, Grand Council Treaty #3 Nibi Declaration, Slave Watershed Environmental Effects Program, and Strategic Assessment of Wood Buffalo National Park. These cases demonstrate how the inclusion of environmental and cultural flows processes in assessment could enable greater water protection.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".