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Record W2981734934 · doi:10.17157/mat.6.3.664

Global health futures?

2019· article· en· W2981734934 on OpenAlexfundno aff
Susan L. Erikson

Bibliographic record

VenueMedicine Anthropology Theory · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
FundersCentre for Global Cooperation ResearchSimon Fraser University
KeywordsBondFutures contractSpeculationEquity (law)Swap (finance)Financial instrumentBusinessObligationCashFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

Since 2010, there has been a discernable expansion of global health financing forms using private equity, bonds, and ‘facilities’ to finance international development and humanitarian endeavors. I present the logics of the Pandemic Emergency Facility (PEF), a World Bank device that lashes together a bond, cash, and swaps to lie in reserve for an infectious disease outbreak. I explain how the PEF is emblematic of financial devices that have the potential to fund global health aid while offering investors a chance to make money. Reckoning with the pandemic bond means that we take account not only of the PEF (what does it organize and by what logics?) but also of the relationships it cultivates (what does it bind together?) and reproduces (what does it aim to multiply and what does it forsake?). I use ‘reckoning with’ as an analytic concept to help us think about measures and futures of global health in both economic and ethical registers, as well as to take account of how death data is used. Reckoning with something lets us pause to take account of where we are and where we are going, and helps us think about what we want. Is it necessary to translate the ethical obligation to help those who are suffering into financial devices that make people money, a trend we are clearly in the initial stages of? Are there conditions when the suffering of others as the source of financial speculation becomes desirable?

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0500.003

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 head0.040
GPT teacher head0.512
Teacher spread0.472 · 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; both teacher heads agree on what is shown here.

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

Citations24
Published2019
Admission routes1
Has abstractyes

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