STRATEGIC ENVIRONMENTAL ASSESSMENT: WHAT IS IT? & WHAT MAKES IT STRATEGIC?
Bibliographic record
Abstract
This paper highlights perhaps one of the most fundamental issues constraining strategic environmental assessment (SEA) practice — its definition. Current reviews fail to explain why certain assessments are referred to as strategic while others are not. Furthermore, there appears to be very little attention given to the basic characteristics of strategy in the environmental assessment of proposed or existing policies, plans and programmes. This paper attempts to identify the characteristics of SEA that make it strategic and therefore different from other forms of impact assessment. A review of selected case studies is undertaken with the purpose of identifying those assessments that actually conform to the characteristics of a "strategic assessment". It is argued here that if SEA methodology and practice is to advance, then a common understanding of its definition and characteristics must first be achieved.
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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".