Where is the avoidance in the implementation of wetland law and policy?
Bibliographic record
Abstract
Many jurisdictions in North America use a “mitigation sequence” to protect wetlands: First, avoid impacts; second, minimize unavoidable impacts; and third, compensate for irreducible impacts through the use of wetland restoration, enhancement, creation, or protection. Despite the continued reliance on this sequence in wetland decision-making, there is broad agreement among scholars, scientists, policymakers, regulators, and the regulated community that the first and most important step in the mitigation sequence, avoidance, is ignored more often than it is implemented. This paper draws on literature published between 1989 and 2010, as well as 33 semi-structured, key-informant interviews carried out in 2009 and 2010 with actors intimately involved with wetland policy in Alberta, Canada, to address key reasons why “avoidance” as a policy directive is seldom effective. Five key factors emerged from the literature, and were supported by interview data, as being central to the failure of decision-makers to prioritize wetland avoidance and minimization above compensation in the mitigation sequence: (1) a lack of agreement on what constitutes avoidance; (2) current approaches to land-use planning do not identify high-priority wetlands in advance of development; (3) wetlands are economically undervalued; (4) there is a “techno-arrogance” associated with wetland creation and restoration that results in increased wetland loss, and; (5) compensation requirements are inadequately enforced. Largely untested but proactive ways to re-institute avoidance as a workable option in wetland management include: watershed-based planning; comprehensive economic and social valuation of wetlands; and long-term citizen-based monitoring schemes.
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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 | 0.036 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.059 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| 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".