Coproducing Flood Risk Knowledge: Redistributing Expertise in Critical ‘Participatory Modelling’
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper suggests that computer simulation modelling can offer opportunities for redistributing expertise between science and affected publics in relation to environmental problems. However, in order for scientific modelling to contribute to the coproduction of new knowledge claims about environmental processes, scientists need to reposition themselves with respect to their modelling practices. In the paper we examine a process in which two hydrological modellers became part of an extended research collective generating new knowledge about flooding in a small rural town in the UK. This process emerged in a project trialling a novel participatory research apparatus—competency groups—aiming to harness the energy generated in public controversy and enable other than scientific expertise to contribute to environmental knowledge. Analysing the process repositioning the scientists in terms of a dynamic of ‘dissociation’ and ‘attachment’, we map the ways in which prevailing alignments of expertise were unravelled and new connections assembled, in relation to the matter of concern. We show how the redistribution of knowledge and skills in the extended research collective resulted in a new computer model, embodying the coproduced flood risk knowledge.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it