Failed Experiments: An Empirical Assessment of Adaptive Management in Alberta's Energy Resources Sector
Bibliographic record
Abstract
Much has been written about adaptive management in the past decade. Broadly understood as an iterative approach to environmental problems wherein management actions are designed as experiments with a view towards learning and improvement, adaptive management’s implementation in Canada, as elsewhere, has been described predominantly as lacking in rigour – what leading U.S. scholars have termed ‘a/m-lite’. This paper contributes to this scholarship by providing an assessment of its implementation and effectiveness in Canada’s energy resources sector and specifically within the province of Alberta, home to Canada’s controversial oil sands. Using freedom of information processes, publicly available documents, and communication with the relevant regulator, the author collected the environmental impact statements, statutory approvals and required follow-up reports for thirteen energy projects (coalmines, oil sands mines, and in situ oil sands) wherein the proponent proposed adaptive management for at least one environmental issue or problem. Content analysis of these documents was conducted to determine the conception, implementation, and effectiveness of adaptive management with respect to each project throughout the regulatory cycle. The results confirm long-standing concerns about the implementation of adaptive management: varying conceptions, including as a routine strategy that guarantees positive environmental outcomes; insufficient attention being paid to experimental design; and no or incomplete implementation. Bearing in mind the ubiquity of adaptive management in energy and natural resources development, the paper concludes with recommendations for law and policy reform. The focus is on Alberta and Canada but the discussion should be relevant to other jurisdictions where adaptive management is prominent, such as the United States and Australia.
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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 | 0.103 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".