The evaluation of assessment: post-EIS research and process development B.SADLER
Bibliographic record
Abstract
During the past fifteen years, environmental impact assessment (EIA) has been, adopted in various parts of the world in order to analyse and mitigate the effects of development proposals. The legislative and institutional frameworks for applying this approach vary considerably among countries and even within federal states, such as the United States and Canada (O’Riordan & Sewell 1981). As a formal procedure, EIA is distinguished by certain characteristics which are common to most, if not all, systems. It is, above all, a predictive exercise directed at the identification and evaluation of the significance of potential changes induced by programmes, projects and activities (see Munn 1979). The emphasis understandably and, perhaps, inevitably is on pre-decision analysis leading to the preparation of an environmental impact statement (EIS) or similar document which establishes terms and conditions for project approval.
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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 | 0.033 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".