Where is the avoidance in the implementation of wetland law and policy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it