Remedies for improving Great Lakes Remedial Action Plans: A Policy Delphi study
Bibliographic record
Abstract
Remedial Action Plans continue to be the principal program to operationalize an ecosystem approach to the restoration of degraded locations across the Laurentian Great Lakes called Areas of Concern. Initiated in 1985, the progress of Remedial Action Plans on balance has been slow and disappointing. The Remedial Action Plan program has been continued following revisions to the Great Lakes Water Quality Agreement in 2012 despite very little systematic inspection of its strengths and limitations. Further, the 2012 Agreement calls for a “nearshore framework” with no clarity on the process for understanding place-based governance methods as developed under these Remedial Action Plans. In this context, we conducted a three-round anonymous online Policy Delphi study involving several dozen experts in the development and implementation of Remedial Action Plans from across the Great Lakes basin within government, industry, academia and civil society. Round 1 collected their direct knowledge of the strengths and limitations of Remedial Action Plans. We distilled that knowledge and asked study participants in Round 2 to further reflect on what worked and what did not work in their experience as Remedial Action Plan practitioners. We found an expected diversity of opinion on what ails the program in Round 2, but an unexpected consensus on the desire to move forward with seven governance options that emerged and were ranked by participants in Round 3. Rankings also indicated a consensus that the options were somewhat feasible and likely to succeed as enhancements to the current governance of Remedial Action Plans. Importantly, the results relate to both the structure and attributes of these collaborative processes, and we therefore stress the need to focus on the predominant tendencies and characteristics that underline Remedial Action Plan processes. These findings will have broad significance for evolving place-based nearshore restoration strategies in the Great Lakes and elsewhere as such programs are initiated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".