Environmental Governance in the Great Lakes: Evaluating Institutional Performance and Collaborative Outcomes
Bibliographic record
Abstract
"The Great Lakes are an invaluable natural resource, containing more than one fifth of the world’s surface fresh water by volume and providing drinking water, commerce, and recreation opportunities to millions. They also offer the ultimate laboratory for analyzing collaborative governance of water resources. A combination of land use changes, industrialization, and climate change have led to the emergence of a myriad of environmental issues facing Great Lakes communities. Harmful algal blooms, plastic marine debris, and aquatic invasive species are but a few examples of emerging dilemmas. This study employs the Institutional Analysis and Development (IAD) framework to examine the external factors, internal structures, and policy decisions of the Great Lakes Water Quality Agreement (GLWQA) and the impacts these variables have on environmental outcomes. The IAD framework is applied specifically to Annex I the GLWQA and used to examine three variables that impact program outcomes: the biophysical environment, culture, and institutional rules. Data was acquired via participant observation and government documents produced by the International Joint Commission, U.S. Environmental Protection Agency (EPA), Environment and Climate Change Canada, state and local government agencies, nonprofit organization, and scholarly articles published on the subject. Results indicate that the biophysical characteristics of the resource, communities of people that rely on the Great Lakes, and institutional rules established by the GLWQA all contribute to the policy’s implementation and resulting outcomes."
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 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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".