Bridging the implementation gap: Designing a course of action with Michigan Public Advisory Councils
Bibliographic record
Abstract
The Michigan Department of Environment, Great Lakes, and Energy (EGLE) enlisted the help of three past capstone program participants through the University of Michigan, School for Environment and Sustainability (SEAS). Between 2019 and 2020, graduate students researched how Michigan Public Advisory Councils (PACs) can maximize their effectiveness and stewardship impact within the Michigan Areas of Concern (AOC) program, a regulatory program established to restore polluted water systems within Michigan. Each capstone participant provided several recommendations to achieve these goals, but converting these recommendations into solutions for decision-makers and practitioners is challenging. To address this “implementation gap,” we collaborated with ten Michigan PACs and EGLE to translate these recommendations into implementation plans. We synthesized the 24 cumulative recommendations from the previous capstone participants into a shortlist of eight, which we used throughout an interview process. We divided this process into Phase I interviews with individual PAC members and Phase II community conversations with multiple PAC members to identify each PAC's priority objectives and workshop their implementation. We analyzed interviews both as individual PACs and as a state-wide program using three main codes: progress, interest, and readiness, as well as an auto-coding process to check our work. These analyses showed that PAC members felt they had made the most progress toward recommendations related to PAC structure, community education, and partner organizations. While PAC members varied in their interview responses, most expressed interest in implementing recommendations where there was the greatest opportunity for progress: community education, life after delisting, and PAC recruitment.
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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 | 0.126 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.013 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".