Gearing up impact assessment as a vehicle for achieving the UN sustainable development goals
Bibliographic record
Abstract
This article reflects on the potential for impact assessment (IA) to be a major vehicle for implementing the UN Sustainable Development Goals (SDGs). While it is acknowledged that the SDGs are intended to deliver broader outcomes than IA currently does, we nevertheless argue there is significant convergence between IA and the SDGs, which we explore utilising the key dimensions of sustainability assessment: comprehensiveness, strategicness and integratedness. We conclude that ‘geared up’ IA might be used as a major vehicle to facilitate achievement of the SDGs. However, IA must become more comprehensive and integrated, such that the full suite of SDGs and their relationships, including trade-offs, can be dealt with in a transparent and inclusive way.
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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 | 0.087 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".