Impact through participatory research approaches: an archetype analysis
Bibliographic record
Abstract
Participatory research approaches are often assumed to be effective for addressing sustainability problems that involve a substantial amount of complexity, uncertainty, and conflicting values.The adaptive and integrative character of these approaches engages various scientific and nonscientific actors in collective knowledge production processes.An increasing number of case studies documents pathways to impact triggered by participatory research approaches.However, cumulative learning across cases about the impacts of participatory research projects remains limited to date.One question is of particular interest, namely how and when different intensities of actor interactions in participatory research effectively contribute to advancing sustainable development.In this paper we address this knowledge gap by presenting a meta-analysis of 29 case studies of participatory research projects in agricultural settings.The study protocol follows systematic case retrieval and selection, coding, and data analysis through formal concept analysis.We introduce and utilize a new diagnostic framework to analyze the links between the intensity of actor interactions, sustainability impact goals, context conditions, and sustainability impacts.The results show that three archetypical patterns describe how the 29 case studies report that participatory research projects generate sustainability impacts: learning, knowledge products, and real-world transformations.Impact in all three patterns is consistently associated with higher intensities of interactions, i.e., coproduction and less consultation but not mere information.The most frequently reported impact is learning in a context of resources and environment problems.In this configuration, coproduction of knowledge is mainly used during the second research phase.However, the results also show that coproduction in the final phase of a participatory research project is more often used to achieve the impact of real-world transformations, which presumably involves more complexity and contestation than other impacts.We conclude that participatory research projects, which aim at transformative impacts in complex settings beyond knowledge products and learning, need to sustain high intensities of actor interactions in knowledge coproduction throughout all research phases to achieve their sustainability impact goals.
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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 | 0.066 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.034 | 0.035 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".