Connecting the strategic to the tactical in SEA design: an approach to wetland conservation policy development and implementation in an urban context
Bibliographic record
Abstract
The paper presents an analytical approach to strategic environmental assessment (SEA), focused on bridging the strategic level assessment of policy objectives with tactical planning and implementation. This is done within the context of an applied SEA application for urban wetland policy development and implementation in the fast growing city of Saskatoon, Saskatchewan, Canada. An expert-based strategic assessment framework was developed and applied to assess the potential implications of alternative wetland conservation policy targets on urban planning goals, and to identify a preferred conservation policy target. Site-specific algorithms, based on wetland area and wetland sustainability, were then developed and applied to prioritize individual wetlands for conservation so as to meet policy targets within urban planning units. Results indicate a preferred wetland conservation policy target, beyond which higher conservation targets provide no additional benefit to sustainable urban development goals. The use of different implementation strategies, based on wetland area vs. wetland sustainability, provides operational guidance and choice for planners to meet the policy objectives within neighbourhood planning units, but those choices have implications for local land use and wetland sustainability.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".