Assessing institutions for aquatic ecosystem protection: A case study of the Oldman River Basin, Alberta
Bibliographic record
Abstract
Policies to protect aquatic ecosystems have proven difficult to implement. This is particularly so in semi-arid regions where water supplies are limited and demands high. The failure of such policies has serious consequences both for aquatic ecosystems and for the people who depend on them. This research investigates the factors that shape the development and implementation of policies for aquatic ecosystem protection in a semi-arid region. It does so by integrating insights from political ecology, human ecology and common property scholarship in a novel theoretical framework that helps to unravel the complex web of cultural, historical and political processes underlying environmental institutions. This integrated framework guides an empirical investigation in the Oldman River Basin (ORB), Alberta. Evidence gathered from 72 documents, 56 key informant interviews, and personal observations from 14 conferences, workshops and watershed tours reveals two sets of eight factors that have impeded progress toward aquatic ecosystem protection in the ORB. The first set of factors focuses on broad contextual influences. These include (1) the ongoing decentralization of water management in Alberta; (2) historically-entrenched positions of power; (3) micro-politics among key actors and organizations; (4) cultural history and identity; (5) application of legal mechanisms; (6) existing water infrastructure and allocations; (7) current aquatic ecosystem condition; and, (8) climate change and future water availability. The second set of influences, referred to as implementation factors, explain the limited extent to which aquatic ecosystem protection policies are being implemented. These include (1) clarity of the actors' roles; (2) communication; (3) the definition of key terms; (4) funding and organizational capacity; (5) leadership; (6) the formal institutional environment; (7) data and monitoring; and, (8) public education. An assessment of the relative significance of these two sets of factors indicates that, in many cases, the contextual factors contradict the course of action recommended by study participants and in the documents reviewed for overcoming the barriers identified as factors affecting implementation. Alternative recommendations are made which have major implications for water management in the ORB. In addition, these recommendations speak to the importance of considering context in human-environment research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".