Interdisciplinary Research as an Iterative Process to Build Disaster Systems Knowledge
Bibliographic record
Abstract
Disasters occur at the intersections of social, natural, and built environments, and robust understanding of these interactions can only occur through insight generated from different disciplines. Yet, there are cultural, epistemological, and methodological differences across the many disciplines concerned with hazards and disasters that can make conducting interdisciplinary research difficult. Approaches are needed to overcome these challenges. This article argues that interdisciplinary disaster research can be successful when it entails an iterative process in which researchers from different disciplines work collaboratively and exert reciprocal influence to generate disaster systems knowledge. Disaster systems knowledge is interdisciplinary and is defined as a comprehensive understanding of the intersections of built, natural, and human environmental factors and their interplay in hazards and disasters. The iterative process can reduce disciplinary biases and privileges by encouraging collaboration among researchers to help ensure disciplinary knowledge complements other disciplinary knowledge, to ultimately generate interdisciplinary disaster systems knowledge. The article concludes by illustrating the process by analyzing a research case study of an interdisciplinary approach to volcanic risk reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.099 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".