Improving impact assessment efficiency: Connecting research and practice in times of change
Bibliographic record
Abstract
The current push towards increased efficiency in impact assessment (IA) creates new opportunities for research to inform future practice. This includes investigating approaches for gaining efficiency at various stages of IA conduct and reporting, and for evaluating their feasibility, utility and how they may be applied without compromising IA quality and effectiveness. This article highlights some potential areas for such research to help address current efficiency issues and needs in IA practice. • Enhancing IA efficiency will require innovation in future IA conduct and reporting. • This presents new opportunities for research to inform and improve IA practice. • This article suggests areas for future research that span various IA components.
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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 | 0.229 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.033 | 0.053 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".