Stewardship after delisting: Sustaining long-term progress in Michigan’s Areas of Concern
Bibliographic record
Abstract
The Michigan Areas of Concern (AOC) program has made significant progress in recent years following the influx of external funding from the Great Lakes Restoration Initiative and the Great Lakes Legacy Act. However, as more AOCs near delisting, community members from Michigan Public Advisory Councils (PACs) are concerned that the loss of programmatic funding will constrain their ability to sustain key public engagement and long-term restoration progress. In order to understand the local community perspectives surrounding delisting, our study presents findings and recommendations that emerged from interviews with Michigan PAC members. We found that PACs recognize the need to transition away from projects with a short-term focus and instead prioritize longer-term, holistic strategies that could help catalyze effective public engagement and produce transformative community revitalization. This study’s recommendations for the Michigan Department of Environment, Great Lakes, and Energy (EGLE) include: (1) dedicating more time to post-delisting planning, (2) enhancing communication efforts with PACs, and (3) strengthening long-term public engagement efforts and PAC organizational capacity. These recommendations add to the growing literature supporting the value of local community perspectives and social dimensions of environmental restoration and may also provide transferable insights to communities outside of Michigan that are currently engaged in similar complex, multi-stakeholder environmental restoration projects.
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 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 | 0.005 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".