The significance spectrum and EIA significance determinations
Bibliographic record
Abstract
The concept of significance is fundamental to environmental impact assessment (EIA). Even though there are many guidelines describing technical characteristics of impacts (such as magnitude, geographic extent, extent and frequency) that should be considered, there has remained a long-standing need for increased clarity on how significance determinations are ultimately reached by significance determiners, those who, on behalf of governments, make a legal determination of significance in EIAs. This involves the application of societal values, in the form of subjective informed judgement, about the acceptability of the predicted impacts. This paper introduces the significance spectrum, a graphic model that illustrates a process for determining significance, using the following steps: (1) determining the threshold of significance for each valued component; (2) weighing the evidence and considering predicted impacts; (3) deciding which side of the threshold the predicted adverse impact falls on; and (4) for unacceptable impacts, deciding if mitigations can make the residual impact acceptable. Concepts such as ecological significance should not be confused with significance in EIAs, which may not only include ecological significance but also considers societal values. We provide specific steps for determining significance that help clarify this fundamental aspect that lies at the core of EIA decision-making.
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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 | 0.037 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".