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Record W7127207311

Teaching Information Literacy in the Emerging and Dynamic Fields of Preparedness and Security

2025· article· W7127207311 on OpenAlexaboutno aff
Abigail D. Adams

Bibliographic record

VenueScholars Archive - University at Albany (University at Albany, State University of New York) · 2025
Typearticle
Language
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessCivil defenseEmergency managementNatural disasterInformation securityInformaticsCrisis managementNational securityRisk management
DOInot available

Abstract

fetched live from OpenAlex

Emergency preparedness and security are comparatively new academic fields, but they have grown tremendously in the last ten years.1 Colleges and universities all over the United States and Canada are adding and expanding programs dedicated to various facets of these subjects. While the specific subtopics and focal areas can vary by program, they all explore threat assessment, risk management, and disaster mitigation in some capacity. Emergency preparedness or emergency management programs prepare students to pursue careers dealing with crisis mitigation and response, whether that crisis is a result of natural disaster, climate change, war, civil unrest, infrastructure failure, economic instability, cyberattack, or some other cause. Security programs similarly address risk and mitigation, although those programs and classes tend to focus more directly on human-caused threats such as terrorism, radicalization, and violence. Often, preparedness and/or security programs align with companion studies in environmental management, international relations, informatics and data analysis, public health, and so on. However, because preparedness and security have only recently been considered as independent fields of study, they lack the reservoir of resources available to better established academic areas.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0000.004
Open science0.0020.003
Research integrity0.0000.001
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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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