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Record W4241922109 · doi:10.31857/s268667300017221-3

AFRICOM versus Ebola

2021· article· en· W4241922109 on OpenAlexaff
Valentin Parkhomenko

Bibliographic record

VenueUSA & Canada Economics – Politics – Culture · 2021
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsInstitute for Christian Studies
Fundersnot available
KeywordsEbola virusEbola vaccinePolitical scienceSoftware deploymentAdministration (probate law)OutbreakNational securityPublic administrationEngineeringVirologyLawMedicine

Abstract

fetched live from OpenAlex

The outbreak of infectious which was caused by the Ebola virus be one of the most humanitarian crises that the international community has encountered in recent years. In early September 2014 it was recognized in the USA as a threat to their national security, putting President Obama administration to the need for urgent measures to neutralize it. Since Liberia was the most affected by Ebola in terms of human losses, the decision was adopted in Washington to seed to this African Command (AFRICOM), numbering about three thousand troops to provide its citizen with humanitarian, engineering and medical care. The article reveals the disconnect between the construction of affected with Ebola and the deployment of US combat-oriented troops.

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)0.003
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.007
Scholarly communication0.0050.004
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.026
GPT teacher head0.280
Teacher spread0.254 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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