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Record W4313567203 · doi:10.1177/028072701803600301

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>

2018· article· en· W4313567203 on OpenAlexaff
Timothy J. Haney, William E. Lovekamp

Bibliographic record

VenueInternational Journal of Mass Emergencies & Disasters · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsMount Royal University
Fundersnot available
KeywordsScholarshipExperiential learningService-learningEngineering ethicsCurriculumDisaster researchMathematics educationPolitical scienceEngineeringSociologyPedagogyPsychologyManagement

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.340
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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