International perspectives on the strengthening of impact assessment through integration and focus
Bibliographic record
Abstract
This paper consolidates international responses to the argument that there is a need to strengthen impact assessment (IA) through greater integration and focus. It is based on invited reflections by various international authors in the field of IA. The main conclusions are that power and context are important underlying reasons for the diversity of IA types; that in certain instances IA legislation works against achieving integration and focus; and that there is a pressing need to be able to measure and demonstrate added value and effectiveness in practice. The paper concludes by setting a research agenda reflecting the need to better understand why diversity exists in IA, what stakeholders expect from the process and how to improve practice based on greater understanding of what the various types of IA deliver.
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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 | 0.128 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".