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Record W4281918129 · doi:10.34172/ijhpm.2022.7212

Ensuring Global Health Equity in a Post-pandemic Economy

2022· editorial· en· W4281918129 on OpenAlexaff
Ronald Labonté

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2022
Typeeditorial
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsOttawa Public Health
FundersUniversity of Massachusetts AmherstUniversity of the PhilippinesUniversity of Surrey
KeywordsAusterityLife expectancyEquity (law)EconomicsPandemicConsumption (sociology)DegrowthCapitalismPoliticsPolitical economyDevelopment economicsEconomic policyPolitical scienceCoronavirus disease 2019 (COVID-19)SustainabilitySociologyInfectious disease (medical specialty)DiseaseLaw

Abstract

fetched live from OpenAlex

With coronavirus disease 2019 (COVID-19) receding, many countries are pondering what a post-pandemic economy should look like. Some advocate a more inclusive stakeholder model of capitalism. Others caution that this would be insufficient to deal with our pre-pandemic crises of income inequality and climate change. Many countries emphasize a 'green recovery' with improved funding for health and social protection. Progressive tax reform and fiscal policy innovations are needed, but there is concern that the world is already tilting towards a new round of austerity. Fundamentally, the capitalist growth economy rests on levels of material consumption that are unsustainable and inequitable. More radical proposals thus urge 'degrowth' policies to reduce consumption levels while redistributing wealth and income to allow the poorer half of humanity to achieve an ethical life expectancy. We have the policy tools to do so. We need an activist public health movement to ensure there is sufficient political will to adopt them.

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)0.012
metaresearch head score (Gemma)0.043
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.022
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.043
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0050.007
Scholarly communication0.0190.010
Open science0.0030.003
Research integrity0.0220.030
Insufficient payload (model declined to judge)0.0100.004

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 head0.044
GPT teacher head0.433
Teacher spread0.389 · 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
GenreEditorial

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

Citations30
Published2022
Admission routes1
Has abstractyes

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