Ecosystem Services and the Political Economy of Watershed Governance
Bibliographic record
Abstract
Water is a multi-use resource, with governance being shaped by a range of interacting institutional and economic imperatives.These many uses comprise a joint system where water and economy are linked by varying degrees of dependency on water-based ecosystem services.The production of ecosystem services is contingent on underlying ecological processes.However these processes can be affected by our actions, creating a sustainability dilemma.Institutions are in place to manage the impacts of our actions; however, institutions are subject to a range of pressures including actor preferences, historical factors, socio-cultural narratives, the influence of ideas and experts, and political context.Thus, we have multiple co-existing and competing resource regimes, drawing on an underlying resource system (water), across a shared landscape, with interrelated institutional mechanisms that are shaped by an array of factors.Moreover, water resources governance is highly normative as multiple actors engage in political contestation and seek to privilege their individual interests within institutional outcomes.Through three separate but interrelated studies, my thesis argues that more attention needs to be paid to the values and normative dimensions that underlie water governance.The first study develops a decision-support tool that helps planners understand the critical linkages between economic activities and ecological factors at the watershed level.The analysis uses Ontario's Mississippi Valley as an illustrative case.The second study draws on natural resource economics and models of participatory governance in order to examine how cultural values affect water management in the case of Chelsea, Quebec.The third study treats the Ontario Clean Water Act (2006) as a case study.It traces actor involvement in the development of the legislation, examining subsequent institutional changes, and how
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".