Toward science-informed public policy: A conceptual framework for contributing to and studying Great Lakes coastal shoreland management
Bibliographic record
Abstract
Great Lakes coastal shorelands encompass valuable environmental and social resources. Most are privately owned. Governments play an important role in managing the use of those shorelands to ensure adequate conservation of the natural and social benefits they provide. Scientists have demonstrated that imprudent land uses are yielding significant ecological harms and increased risks to coastal shorelands, and yet those uses persist. Public coastal shoreland management appears to be poorly informed by the best available science. In addition to generating good science, scientists are themselves members of the public well-positioned to contribute to improved coastal shoreland management. Two prominent proposals for doing so include calls for scientists, first, to better communicate their knowledge through direct engagement with decision-makers (‘contributing to’) and second, to co-produce the knowledge that decision-makers require by participating in multi-disciplinary, community-engaged research (‘studying’). For either endeavor, scientists need to understand public coastal shoreland management processes to engage effectively with them. Drawing from multiple literatures, this paper presents a conceptual framework to assist scientists working to contextualize and more effectively convey the knowledge they have, or to engage in research designed to co-produce knowledge, in order to better promote science-informed public coastal shoreland management. The framework is set within the institutional arrangements that structure coastal management processes, and it highlights the ways in which key decision-maker attributes—their collective knowledge, capacities, and commitments—influence decision-making actions and outputs. While situated specifically within the context of coastal management, the framework is adaptable to other policy arenas more broadly.
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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 | 0.059 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.015 | 0.108 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.021 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".