Bibliographic record
Abstract
Teaching crisis management is both fascinating and frustrating. It is fascinating because crises, by their very nature, are spectacular, dramatic, and intense; immediately arouse the individual and collective imagination; and because everyone seeks explanations for what, at first glance, appears inexplicable. It is also fascinating because educators are exposed to a transdisciplinary and transborder field of studies with wide-ranging ramifications. Yet it is frustrating because educators must often deconstruct the popular perception that crises are rare, improbable, and unpredictable phenomena, often leading individuals to feel powerless and fatalistic. It is also frustrating because of the lack of knowledge in the field itself, at three levels: conceptual/theoretical, practical, and reflective. This article highlights the teaching challenges in this rich and diversified field at each of these three levels and examines three teaching tools to address them: case studies, crisis simulations, and the reflexive journal. The authors also consider that a crisis cannot be viewed as a homogeneous concept. With the help of Gundel’s crisis typology (conventional, unpredictable, intractable, and fundamental crises), the authors present promising teaching approaches to deal with each of the three aforementioned teaching challenges, explaining how each approach can be seen as a function of the four types of crises.
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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".