On the Margins, No More: Teaching and Learning as a Core Concern of Disaster Scholarship <i>Introduction to the IJMED Special Issues on Teaching and Learning</i>
Bibliographic record
Abstract
This paper introduces two special issues of the International Journal of Mass Emergencies and Disasters focused on teaching and learning. Though there is much recent literature on teaching from other fields, the hazards and disasters community has produced little written scholarship on pedagogy and on curriculum design. To address this gap, we produced a call for papers for a special issue and received many submissions. In this paper, we introduce the need for more scholarship on teaching and learning on the hazards and disaster field. This includes classroom exercises, experiential learning activities, service-learning and citizen science approaches, and explorations of curricular design. We also introduce the papers that make up the two special issues. The first issue focuses on “Innovative Teaching Techniques and Practices in Hazards and Disaster Studies” and the second on “Curricular Innovations in Hazards and Disaster Studies.” We hope that the papers contained in these two special issues will creat a sustained dialogue on best practices in teaching about hazards and disasters.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".