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Record W2587073673 · doi:10.1016/s2214-109x(17)30041-4

The Ebola suspect's dilemma

2017· article· en· W2587073673 on OpenAlexaboutno aff
Eugene T Richardson, Mohamed Bailor Barrie, Cameron T. Nutt, J. Daniel Kelly, Raphael Frankfurter, Mosoka Fallah, Paul E. Farmer

Bibliographic record

VenueThe Lancet Global Health · 2017
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of General Medical Sciences
KeywordsDilemmaSuspectEbola virusSierra leonePrisoner's dilemmaEthical dilemmaCriminologyMedicineLawPsychologyPolitical scienceSociologyVirologyOutbreakEpistemologyPhilosophySocioeconomics

Abstract

fetched live from OpenAlex

In 1950, Merrill Flood and Melvin Dresher of the RAND Corporation developed a theoretical model of cooperation and conflict, which was later formalised by Albert W Tucker as the prisoner's dilemma.1 This model represents a situation in which two prisoners each have the option to confess or not, but their sentencing outcomes depend crucially on the simultaneous choice of the other (figure).1 Fittingly, it has become the paradigmatic example of individual versus group rationality and is an often used heuristic when conveying introductory social theory to students.

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)0.009
metaresearch head score (Gemma)0.025
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.022
Scholarly communication0.0060.010
Open science0.0010.006
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0070.001

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 head0.080
GPT teacher head0.471
Teacher spread0.391 · 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
GenreCommentary

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

Citations37
Published2017
Admission routes1
Has abstractyes

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