Building Great Lakes Resiliency to Eutrophication: Lessons to inform adaptive governance of the nearshore areas of the Laurentian Great Lakes.
Bibliographic record
Abstract
Annex 2 of the Great Lakes Water Quality Protocol calls for the collaborative development of a ‘nearshore framework’, but does not provide guidance with respect to nearshore governance. This thesis bridges this gap with a series of studies on the determinants for adaptive governance that will inform implementation of the Great Lakes Water Quality Protocol 2012. The principal focus of this work is on eutrophication, which is essentially a nearshore issue. The methodology includes a comprehensive literature review and 35 key informant interviews using a standardized questionnaire. The results assess Great Lakes governance, examine the strengths of the Great Lakes Water Quality Agreement Protocol 2012 and evaluate the effectiveness of the International Joint Commission. A major product of the research is the development of a framework for assessing adaptive capacity based on six determinants: public participation, science, networks, leadership, flexibility and resources. The framework is validated in the case study of eutrophication in Lake Erie and used to identify gaps in adaptive capacity for current eutrophication governance of Lake Erie. The framework was then tested on two additional case studies, the Chesapeake Bay and the Baltic Sea Region. These systems are both eutrophic and are similar in many other ways to the Great Lakes. This allowed exploration of issues of scale, from local (Chesapeake Bay) to binational (the Great lakes) to transnational (the Baltic Sea). The most important finding of this work is that the key barrier for building adaptive capacity for eutrophication governance in the Great Lakes is the lack of adequate leadership and resources. A key recommendation is therefore that the IJC be strengthened in its role to function as a collaborative leader to foster adaptive capacity. The findings from this research can inform the implementation of the Great Lakes Water Quality Protocol 2012.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.009 |
| 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".