IMPACT ASSESSMENT RESEARCH: ACHIEVEMENTS, GAPS AND FUTURE DIRECTIONS
Bibliographic record
Abstract
Welcome to this special JEAPM issue on impact assessment (IA) research, which — besides this introductory paper — includes 16 short papers contributed by a wide range of leading IA researchers from around the world. These papers provide for an overview of achievements, gaps and future directions for IA research. The collection of papers is the outcome of a targeted call to researchers representing a wide range of IA areas and regions. This has resulted in what we believe is an impressive compilation of contributions on environmental impact assessment (EIA), strategic environmental assessment (SEA), health impact assessment (HIA) and social impact assessment (SIA) as well as theoretical, applied and normative aspects of IA, with a particular focus on sustainable development from European, North and South American, Asian, African and Australian authors.
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 | 0.080 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".