Centering Communities in Great Lakes Restoration and Ecosystem-based Management Programs – Report to Healing Our Waters Coalition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it