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Record W3033870810 · doi:10.9745/ghsp-d-20-00217

Will the Higher-Income Country Blueprint for COVID-19 Work in Low- and Lower Middle-Income Countries?

2020· editorial· en· W3033870810 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Health Science and Practice · 2020
Typeeditorial
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsAlberta HealthUniversity of Alberta
Fundersnot available
KeywordsBlueprintLow and middle income countriesMiddle incomeCoronavirus disease 2019 (COVID-19)Work (physics)PovertyMiddle income countryIncome distributionBusinessDeveloping countryDemographic economicsDevelopment economicsEconomicsEconomic growthInequalityMedicine

Abstract

fetched live from OpenAlex

<h3>Key Message</h3> Strategies currently pursued in high-income and upper middle-income countries—aimed at radically suppressing incidence of COVID-19—may be unrealistic and counterproductive in most low- and lower middle-income countries. Instead, strategies need to be tailored to the setting, balancing expected benefits, potential harms, and feasibility.

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.

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)0.020
metaresearch head score (Gemma)0.175
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.175
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.143
GPT teacher head0.482
Teacher spread0.339 · 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