Setting an agenda to catalyze research in the social and organizational dimensions of Great Lakes remediation, restoration, and revitalization
Bibliographic record
Abstract
The Great Lakes region was once a hub of industry and innovation that provided wealth and identity to the region. Economic upheavals have left the region trying to recreate economies and cleanup degraded environments. There have been multiple, overlapping efforts to change these conditions and create a new narrative for the region through environmental remediation, habitat restoration, and community revitalization on the path towards resilience. The elements that contribute to success are organized differently in different places, and are not always identified or characterized in the environmental literature. Trying to fill this conceptual gap is critical because landscape-scale environmental cleanup has been delivered at the local scale through various partnerships and arrangements. Thus, this special collection of articles in the Journal of Great Lakes Research explores how individuals, organizations, and communities are engaging in the complex process of environmental cleanup and revitalization throughout the region. This collection of articles represents a range of approaches to unpack how people are navigating and contributing to this regenerative process from quantitative studies at the regional scale that characterize global patterns to in-depth qualitative studies that identify and characterize the processes that unfold in specific places to change our environments both ecologically and socially. These articles represent the broad experience unfolding in the region to understand these activities through research and navigate them through practice. This collection will add new dimensions to Great Lakes research by including the individuals, organizations, and agencies as components of the ecosystem.
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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 | 0.074 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.023 | 0.043 |
| Scholarly communication | 0.038 | 0.047 |
| Open science | 0.005 | 0.044 |
| Research integrity | 0.022 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".