Title: Water Governance Process Assessment: Evaluating the Link between Decision Making Processes and Outcomes in the Columbia River Basin Abstract approved: ______________________________________________________
Bibliographic record
Abstract
Academics and practitioners agree that in water governance, the quality of a decision making process should influence the quality of the outcome and the degree to which it is accepted by interested parties. However, finding a feasible way to evaluate and then improve the quality of a decision making process has proven elusive. Systematically collecting evidence of a link between process and outcome is also challenging. In my dissertation, I developed a synthesis framework for evaluating and improving water governance decision making to address these two challenges. The synthesis framework, which I call the Water Governance Process Assessment (Water GPA), draws upon 22 existing frameworks rooted in resilience, adaptive governance, and good governance. From these frameworks, I identified and provided a way to evaluate four characteristics critical to good water governance decision making processes: 1) accountability, 2) inclusivity, and 3) information, and 4) context. I applied the Water GPA framework to the recent reviews of the Columbia River Treaty by the United States and Canada. I collected data for the case studies through semi-structured interviews and surveys of process participants from the federal agencies,
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 | 0.023 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".