Eutrophication and water quality policy discourse in Lake Erie Basin
Bibliographic record
Abstract
Watershed-based approaches to addressing water quality issues often involve a diverse set of actors working collaboratively to develop policy. Such an approach is currently underway in the Western Lake Erie Basin, where the province of Ontario and the state of Ohio have embarked on a 40% phosphorus run-off reduction target to address eutrophication problems in the lake. In this study, we adopt the concept of discourse to inform our understanding of the collaborative process undertaken to develop domestic action plans (DAPs) to guide efforts by various stakeholders. We find that in both cases there were distinct groups of actors who shared and promoted a particular narrative or storyline. These storylines provided varying accounts of the science and policy aspects of the eutrophication problem in Lake Erie, and there was variation as well in the specific actors to whom they attributed responsibility. We illustrate how the storylines shaped the nature and form of the action plans. We provide a discussion of the policy implications of unequal capacities among different actor coalitions to influence trajectories and outcomes in the context of governance for water quality. It is shown that the potential of discourse coalitions to influence policy raises important questions as to whose voice is considered legitimate enough to be included in the policy process.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".