Defining community revitalization in Great Lakes Areas of Concern and investigating how revitalization can be catalyzed through remediation and restoration
Bibliographic record
Abstract
An international effort to restore contaminated areas across the Great Lakes has been underway for over 50 years. Although experts have increasingly recognized the inherent connections between ecological conditions and community level benefits, Great Lakes community revitalization continues to be a broad and complex topic, lacking a comprehensive definition. The purpose of this study was to generate a testable “AOC-Revitalization Framework” for linking remediation and restoration success, represented by Beneficial Use Impairment (BUI) removal in U.S. Great Lakes Areas of Concern (AOC), to community revitalization. Using directed content analysis, we conducted a literature review and identified 433 potential revitalization metrics and indicators and grouped them into 15 broader community revitalization attributes to develop the following definition of Great Lakes community revitalization: “locally driven community resurgence resulting in resilient and equitable enhancements to social, economic, and environmental community structures.” We surveyed experts within the Great Lakes AOC program on the likelihood remediation and restoration success, would positively impact revitalization attributes. Focus groups triangulated survey results. Results identified BUI removal was expected to positively affect revitalization, but the type of revitalization outcome was based on the BUI being removed. The AOC-Revitalization Framework is the first to empirically outline these possible linkages, providing a clear testable structure for future research; it can be used to better understand how environmental improvements are or are not leading to community revitalization and more accurately identify components of revitalization impacted, thus supporting more equitable representation, communication, and measurement of the relationship.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".