Disaster Education in the Context of Postsecondary Education: A Systematic Literature Review
Bibliographic record
Abstract
The upward tendency of global disasters and associated losses urgently calls for enhancing disaster education (DE) and professional training. Although postsecondary education institutions (PSEIs) play an essential role in this educational mission, a comprehensive understanding of DE development in PSEIs remains unclear. In response to this knowledge deficit, this article employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to explore the current development of DE in PSEIs through reviewing 76 publications from 2011 to 2022 through a mixed-method analysis process (univariate analysis and thematic inquiry). The quantitative analysis outcomes illustrate that the studies were unevenly distributed globally. The qualitative findings demonstrate the following fourfold characteristics of DE in PSEIs: (1) in terms of pedagogical characteristics of DE, there is a call for a radical shift in pedagogical strategies because the conventional teaching approaches are inadequate in tackling the potential risks and emerging hazards; (2) disciplinary and interdisciplinary perspectives have been integrated into DE, addressing diverse disasters and their differential influences; (3) through community-based partnerships, PSEIs can provide informal and nonformal education to affected individuals and communities; and (4) interconnections between DE and other educational approaches that broadly address global climate change have been recognized, contributing to resilience and sustainability. Based on these outcomes, this study recommends future DE-specific research could focus on (1) exploring potential radical pedagogical approaches to respond to emerging risks and hazards, (2) stimulating cross-disciplinary collaboration to address the full spectrum of disasters’ societal impacts, and (3) increasing collaboration between PSEIs and nonacademic organizations to raise grassroots awareness through informal DE.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".