Achieving health impact and materialising air pollution: how science-policy interfaces were achieved in an inter-disciplinary project
Bibliographic record
Abstract
Societal impact is an increasingly important imperative of academic funding. However, there is little research to date documenting how impact is accomplished in practice. Drawing on insights from Actor Network Theory, we explore the research-policy interface within an inter-disciplinary research project on the relationships between air pollution and human health. Health policy impact was important to the researchers for moral as well as pragmatic reasons, but it was a goal that was seen as potentially in tension with that of doing science. In fields such as air pollution and health, networks of policy makers and researchers are inevitably entangled and processes of engagement operated to delineate ‘science’ from ‘policy’. ‘Health’ was initially black boxed and under-explicated, used as a signifier in itself for societal impact. By mobilising networks of policy actors, brought together in workshops to rank the importance of policy scenarios for the research team, the connections between air pollution and health were materialised and made actionable. This was achieved by framing existing data sets, emission technologies, policy expertise, pollutant species and human health in particular ways and, in doing so, excluding others. The process of linking air pollution and health research to achieve societal impact not only influenced how these phenomena were known but, critically, enabled and constrained potential policy responses. Tracing these research arrangements made the material discursive processes of ‘impact’ visible and analysable as objects of social science scholarship, and therefore generated a productive site for critically engaging with processes of environment and health science and policy
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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 | 0.240 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.025 | 0.085 |
| Scholarly communication | 0.039 | 0.043 |
| Open science | 0.006 | 0.049 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".