Toward a critical technical practice in disaster risk management: lessons from designing collaboration initiatives
Bibliographic record
Abstract
Purpose Despite decades of social science research into disasters, practice in the field continues to be informed largely from a technical perspective. The outcome is often a perpetuation of vulnerability, as narrowly defined technical interventions fail to address or recognize the ethical, historical, political and structural complexities of real-world community vulnerability and its causes. The authors propose that addressing this does not require a rejection of technical practice, but its evolution into a critical technical practice – one which foregrounds interdisciplinarity, inclusion, creativity and reflexivity, as means to question the assumptions, ideologies and delimited solutions built into the technical tools for understanding risks. Design/methodology/approach The authors present findings from three events they designed and facilitated, aimed at rethinking the engineering pedagogy and technical practice of disaster risk management. The first was a 2-day “artathon” that brought together engineers, artists and scientists to collaborate on new works of art based on disaster and climate data. The second was the Understanding Risk Field Lab, a 1-month long arts and technology un-conference exploring critical design practices, collaborative technology production, hacking and art to address complex issues of urban flooding. The third was a 4-month long virtual workshop on responsible engineering, science and technology for disaster risk management. Findings Each of these events uncovered and highlighted the benefits of interdisciplinary collaboration and reflexivity in disaster risk modeling, communication and management. The authors conclude with a discussion of the key design elements that help promote the principles of a critical technical practice. Originality/value The authors propose “critical technical practice” which foregrounds principles of interdisciplinarity, inclusion, creativity and reflexivity, as a means to question the assumptions, ideologies and delimited solutions built into the technical tools for understanding climate and disaster risk.
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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 | 0.094 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.043 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".