Developing international guidance to make impact assessment follow-up happen – reflections on an interactive design process
Bibliographic record
Abstract
To advance impact assessment (IA) practice worldwide the International Association for Impact Assessment (IAIA) has long promoted and published a series of international best practice principles, including the recently revised best practice principles for IA follow-up. IA follow-up refers to any kind of undertaking that seeks to ‘understand the outcomes of projects or plans’ that have been subject to IA. To support the implementation of these principles worldwide, a global guidance document has been developed (published by IAIA in 2024). The aims of this paper are to report on the approach undertaken to this guidance, applying a Delphi method, and to reflect on the utility and learnings derived from the process. To this end, the method of reflexivity was utilised as well as consideration of the broader literature. Overall, applying the Delphi approach in combination with international workshops helped calibrate international guidance that will be meaningful to a broad audience and relevant for unlocking worldwide experience. A key learning was that establishing and communicating international guidance generates tension between detailed explanations relevant to specific contexts versus generalization and overview.
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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 | 0.205 | 0.191 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".