Cross-country use of participatory research methods in practice to enhance inclusive decision-making
Bibliographic record
Abstract
Purpose Rethinking participation in disaster research and practice could be facilitated when practitioners are provided with opportunities to pause and reflect deeply on their work outside of the context of their own individual projects and organizational networks. The article draws from an extended collaboration between researchers from multiple countries and disciplines in a working group, which aimed at exploring ethics, participation and power in disaster management. Design/methodology/approach Under responsible engineering science and technology for disaster risk management, the authors undertook weekly meetings over four months to discuss various facets of adopting participatory methods in their individual projects in Nepal, India, the Philippines and the USA. The article develops a critical reflection of practice using an auto-ethnographical and poly-vocal approach. Findings The voluntary, digital, sustained, unstructured, recurring and inter-disciplinary characteristics of the authors' working group created an opportunity for researchers and practitioners from different fields and different national, cultural and linguistic backgrounds to come together and collectively issues related to participation, ethics and power. Research limitations/implications In the paper, the authors do not offer a systematic evaluation of what was a fairly unique process. The paper offers no evaluation of the working group or others like it that focus on questions of replicability, scale and sustainability. Originality/value To the best of the authors' knowledge, the current work is a unique paper that focuses on situating multi-disciplinary practice within disaster risk management (DRM) and enhancing networks, capacities and expertise for professional education for engineers, physical and social scientists who are involved in research and practice. The polyvocal character of the presentation will help readers access the particular experiences of the participants, which reflect the deeply personal character of the subject matter.
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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 | 0.260 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".