Conceptual frameworks for SIA revisited: a cumulative effects study on lead contamination and economic change
Bibliographic record
Abstract
This article trials three conceptual frameworks on an Australian case study of a small remote city suffering lead contamination, with cumulative effects from concurrent economic change due to downsizing in the mining industry. It interprets the usefulness of these frameworks, and explores two questions: can they apply to circumstances other than project assessment, and what are their relative merits as guides to SIA? All the frameworks reviewed can be used in non-project and cumulative SIA, although, if they had been used to predict the impacts in our case study, we may easily have been misled as to the resilience of the community. Choosing among these frameworks becomes a matter personal preference: each has different merits.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".