Behavioural Sciences Used by the United Nations to Achieve the Sustainable Development Goals: A Roadmap and Some Stop Signs
Bibliographic record
Abstract
The United Nations (UN) and several UN Agencies have started to use behavioural sciences in order to achieve their policy goals, including for achieving the Sustainable Development Goals (SDG). While it is appreciated that insights into actual behavior inform the policymaking of international actors, they also raise scientific and normative considerations that warrant caution. First, for those considerations it matters, who the acting and the targeted actors are: behavioural interventions come in many facets and warrant a differentiated view – a finely built roadmap is thus desirable. Second, there are concerns about the internal and external validity of experimental research on which behavioural sciences largely, but not solely, draws. Third, taking a differentiated view on behavioral sciences also allows for a more finely grained view on normative concerns underlying the operations of the United Nations in environmental policy. This contribution spells out those considerations while still advocating for the approach as such.
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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 | 0.050 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.023 |
| 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".