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Record W3014684743 · doi:10.1016/s1473-3099(20)30150-x

Will financial innovation transform pandemic response?

2020· article· en· W3014684743 on OpenAlexaff
Susan L. Erikson, Leigh Johnson

Bibliographic record

VenueThe Lancet Infectious Diseases · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPandemicDemocracyPolitical scienceGuardianBusinessLawCoronavirus disease 2019 (COVID-19)MedicineDiseasePoliticsInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The mounting death toll from COVID-19 recently prompted The Guardian to declare, “The World Bank's $500m pandemic scheme accused of ‘waiting for people to die’”.1 Similarly, as the number of deaths from Ebola increased in the Democratic Republic of Congo (DRC), there was outrage in prominent journals. “The World Bank has the money to fight Ebola but won't use it” wrote Garrett in Foreign Policy.2 Others3–7 too describe the malfeasance of the financial innovation called the pandemic bond. Hailed by former World Bank president Jim Kim as an instrument that “would rapidly respond to future outbreaks by delivering money to countries in crisis”,8 critics judge the bond harshly, raising many points we agree with.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.273
Teacher spread0.213 · 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.

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

Citations19
Published2020
Admission routes1
Has abstractyes

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