Evolving Institutional Arrangements for Use of an Ecosystem Approach in Restoring Great Lakes Areas of Concern
Bibliographic record
Abstract
The 1987 Canada-U.S. Great Lakes Water Quality Agreement required Remedial Action Plans (RAPs) be collaboratively generated between local stakeholders and government agencies to implement an ecosystem approach in cleaning up 43 historically polluted Areas of Concern (AOCs) throughout the Laurentian Great Lakes. The institutional arrangements that have emerged over the past 35 years to foster an ecosystem approach in RAPs are expected to have changed over time and be varied in some aspects-reflecting unique socio-ecological contexts of each AOC-while also sharing some characteristics that were either derived from the minimally prescribed framework or developed convergently. Here we surveyed institutional arrangements to describe changes over time relevant to advancing an ecosystem approach in restoring beneficial uses in the 43 AOCs. While eight AOCs evidenced little institutional change, the remaining 35 AOCs demonstrated a growing involvement of local organizations in RAPs, which has enhanced local capacity and ownership and helped strengthen connections to broader watershed initiatives. We also noted an expansion of strategic partnerships that has strengthened science-policy-management linkages and an increasing emphasis on sustainability among RAP institutions. Our study details how institutional arrangements in a decentralized restoration program have evolved to implement an ecosystem approach and address new challenges.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".