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Record W2418454519 · doi:10.1017/s1049023x00043302

Complex Emergencies: Expected and Unexpected Consequences

2001· article· en· W2418454519 on OpenAlexaff
Michael J. Schull, Leslie Shanks

Bibliographic record

VenuePrehospital and Disaster Medicine · 2001
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsHealth Sciences CentreEngineers Without Borders CanadaSunnybrook Health Science Centre
Fundersnot available
KeywordsMedical emergencyComputer sciencePsychologyForensic engineeringMedicineEngineering

Abstract

fetched live from OpenAlex

Complex emergencies emerged as a new type of disaster following the end of the Cold War, and have become increasingly common in recent years. Human activity including civil strife, war, and political repression often coexist with and contribute to natural phenomena such as famine. They frequently result in high mortality, population displacement, and the disruption of civil society and its infrastructure. This article reviews the context of recent complex emergencies, and their expected health consequences, such as diarrhea, measles, malnutrition and outbreaks of infectious disease, and the disruption of mechanisms of disease control and surveillance. However, the complex nature of these emergencies also may have unexpected consequences, such as hindering understanding of their causes or limiting the attention paid to them by the public. This paper discusses the context and consequences of complex emergencies from the health standpoint, and explores some of their unexpected effects.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.095
GPT teacher head0.400
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations16
Published2001
Admission routes1
Has abstractyes

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