Transaction costs for collaboration in the watershed management of the Cuyahoga River Area of Concern
Bibliographic record
Abstract
This study examines the transaction costs of collaborative watershed management in the Cuyahoga River Area of Concern (AOC)—one of the 43 geographic areas designated by the U.S. and Canada Great Lakes Water Quality Agreement (GLWQA), where significant impairment of beneficial uses has occurred as a result of human activities. The Cuyahoga River is located in Northeast Ohio, the U. S. and flows through the City of Cleveland before draining into Lake Erie—one of the five Great Lakes of North America. The watershed is degraded due to municipal and agricultural discharges, streambank erosion, and contamination from urban and industrial sources. This research explores how a diverse group of stakeholders convened under the Cuyahoga River AOC Advisory Committee to share information, coordinate activities, agree on activities that restore beneficial uses, and support strategic management actions. In this study, 23 semi-structured interviews with members of the advisory committee were conducted between January 28, 2020 and April 20, 2020, with follow-up emails and phone calls as needed to corroborate information. A review of research articles and government documents supported the interviews, including United States Environmental Protection Agency (U.S. EPA) and Ohio Environment Production Agency (OEPA) reports on the GLWQA and Cuyahoga River Remedial Action Plans. A third source of data is from direct participant observation at quarterly meetings of the advisory committee during 2017–2020, binational AOC conferences in 2017 and 2019, and other professional events geared towards restoring the Cuyahoga River AOC in 2016–2020. Results help to explain the collaborative process within the advisory committee and measure the institutional performance of the advisory committee in terms of efficiency, equitability, accountability, and adaptability. Results of this study include a set of recommendations to help guide group structure and decision-making processes, including (1) employing best available technology to organize AOC events and disseminate information; (2) supporting new members with an orientation and/or mentor to clearly define formal and informal committee rules; (3) assuring equal access to detailed information on management action plans with a real time dashboard; (4) updating voting procedures and the prioritization of management actions; and (5) better incorporating underrepresented local communities and high-level decision makers from municipalities, government agencies, and nongovernmental organizations located within the Cuyahoga River AOC.
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 | 0.000 | 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".