TOWARDS A STRUCTURED APPROACH TO STRATEGIC ENVIRONMENTAL ASSESSMENT
Bibliographic record
Abstract
Considerable attention has been given to the role of strategic environmental assessment (SEA) in policy, plan and program assessment; however, there is very little consensus on an appropriate methodology for SEA. Two basic perspectives on SEA methodology emerge from the literature: first, that appropriate SEA methodologies are readily available based on the application of project-level EIA approaches to strategic assessment questions and second, that SEA requires a different, more broad-brush approach than project-level EIA. If SEA is to advance in application and effectiveness, then appropriate SEA methodologies need to be established. Despite calls for SEA to develop more independently of project-level assessment, existing SEA methodologies tend to be based on project-level EIA principles. It is argued here that while SEA can perhaps utilise many of the existing methods from project-level EIA, it requires a different, more broad-brush, but structured methodological approach. This paper reviews the current state-of-the-art of SEA methodology, and presents a generic SEA methodological framework and example based on the notion of the "best practicable environmental option".
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.
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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".