Community engagement and the importance of partnerships within the Great Lakes Areas of Concern program: A mixed-methods case study
Bibliographic record
Abstract
The Great Lakes Areas of Concern (AOC) program was created through amendments to the Great Lakes Water Quality Agreement (GLWQA) in 1987 to restore contaminated sites using an ecosystem-based approach. This program represents one of the first instances of ecosystem-based management (EBM) in the Great Lakes region with a specific focus on the inclusion of the public and local stakeholders in the process. Despite official language incorporating EBM in the AOC program, implementation of these practices has not been consistent across AOCs given differences in local arrangements of Public Advisory Councils (PACs), approaches to community engagement, and environmental problems. To better understand community engagement in these complex AOCs, this research investigated community, PAC, and state agency perspectives in three AOCs in Michigan: the Kalamazoo River, Saginaw River and Bay, and Rouge River AOCs. We gathered data through interviews, focus groups, and participatory observations with community members, PAC members, and state officials in each AOC. Findings indicate that communities in these areas have minimal connection to the AOC program and PACs. Community members tended to have greater connection to local organizations that provide a variety of opportunities for community members to engage with their environment in ways they value. To better connect the public to the AOC program, PACs may benefit from intentional partnerships with community organizations to increase community engagement. To consistently bolster community engagement in AOCs, we further recommend that state agencies provide additional resources to improve connection to local communities.
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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 | 0.032 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".