Coproducing Flood Risk Knowledge: Redistributing Expertise in Critical ‘Participatory Modelling’
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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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 | 0.049 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.065 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".