From participatory engagement to co-production: modelling climate-sensitive processes in the Arctic
Bibliographic record
Abstract
Participation is increasingly being used in the modelling of climate-sensitive systems to improve usability. Bottom-up, place-based approaches to modelling can challenge the dominantly positivist approaches used until recently. We examined how participation is reported within modelling research that uses participatory approaches, focusing on the Arctic. Our systematic scoping review identified 26 articles that used participatory approaches in modelling research to explore a climate-sensitive process in an Arctic setting and analysed the degree of participation at each stage of the process for each article. A diversity of topics, modelling approaches, and participant groups were identified. Most studies (71%) occurred in Arctic North America, and all studies engaged with non-Western knowledge types to some degree. Participation was most commonly reported at the model generation and participant identification stages, and least commonly reported in the choice of modelling type. Participatory scores — based on the number and degree of participatory stages of a study — were higher where authors gave instrumental or transformative rationales for the use of participation, and among studies that described prioritising non-Western knowledge types. Detailed reporting of participatory processes was frequently absent, suggesting a need for clearer discussions of these issues in the descriptions of the process.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".