Centering Communities in Great Lakes Restoration and Ecosystem-based Management Programs – Report to Healing Our Waters Coalition
Bibliographic record
Abstract
A notable transformation is occurring across the US, Canada, and the globe, reframing “ecosystem restoration” as more than technical actions that improve the environment, but also as collective actions that explicitly acknowledge and include the human and social systems that coexist with biophysical systems. There is also increasing attention directed towards involving local communities in regional landscape restoration and conservation for both planning and long-term stewardship, to help ensure that ecosystems and their component communities are more resilient in the face of increasingly challenging stressors (e.g., legacy contamination, climate change effects, severe weather, and economic instability). This report provides: (1) an expanded science-, knowledge-, and practice-based narrative for Great Lakes Restoration that includes emphasis on community revitalization (i.e., increasing community agency and vitality, and fostering equity), based on integrated socio-ecological visions for the region; and (2) a set of prioritized implementation strategies to facilitate the systemization of this work. The impact of this research is to synthesize the results of a workshop held May 17-19, 2023, on how Great Lakes environmental programs can contribute to community and Indigenous well-being by considering and improving community capacity, broadening the scope of environmental education, developing qualitative and quantitative metrics of well-being, and broadening opportunities for cross-agency learning with Indigenous governments.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".