WORKSHOP APPROACH TO DEVELOPING OBJECTIVES, TARGETS AND INDICATORS FOR USE IN SEA
Bibliographic record
Abstract
Strategic Environmental Assessment (SEA) is the process through which the impacts of plans and programmes on the environment are assessed. Objectives, targets and indicators are the tools through which these environmental impacts can be measured. The same objectives, targets and indicators may be used for all planning levels but it is also necessary to identify additional plan specific ones. We used a workshop based approach to provide an interface between planners and environmental scientists and to give examples of objectives, targets and indicators for biodiversity, water, air and climatic factors, which could be used in SEA for national, regional and local plans. In addition, we highlight the need for careful consideration during the selection process of these variables which will result in a more rigorous and robust SEA. This is a challenging process but once completed will maximise resources and reduce the workload later in the SEA process.
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 | 0.039 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".