Can BC's 40-year-old water quality objectives policy solve today's challenges for managing cumulative effects?
Bibliographic record
Abstract
Water quality is a critical component of aquatic ecosystems, and impairments caused by the cumulative effects of human activities can threaten water security, ecosystem health and biodiversity, and ecosystem services that support human livelihoods, health, and well-being. Protecting water quality and managing the human activities that can contribute to cumulative effects remains the most important, though poorly understood and under-researched problem facing sustainable water quality management in Canada (Johns & Sproule-Jones, Schindler & Donahue, 2006) and around the world (Patterson, Smith, & Bellamy, 2013; UN-Water, 2011). For decades, federal and provincial governments in Canada have introduced, and experimented with, policy tools that are intended to assess and manage cumulative effects, yet, point source management approaches remain by far, the preferred policy tool. The results of this study indicate that part of the reason why cumulative effects assessment and management approaches have not evolved is because policy tools intended to address questions about environmental governance are being implemented as environmental management tools. Questions of environmental governance should be inclusive and focused on how the environment is used now and in the future for societal benefits. Conversely, management questions are narrower in scope and serve to operationalize these goals. This research highlights the challenge with identifying and developing critical relationships between the array of agencies and institutions responsible for governing and managing water quality, as well as the need to devise strategies to ensure these relationships are maintained over time if progress towards managing cumulative effects to water quality can be achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".