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Record W4376506149 · doi:10.18356/9789210027090c002

Executive Summary

2023· book-chapter· en· W4376506149 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUnited Nations eBooks · 2023
Typebook-chapter
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicDevelopment economicsTollQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)RecessionEconomic growthDistribution (mathematics)Developing countryPolitical scienceGeographyEconomicsMedicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

While most of the countries across the world are recovering from a severe recession resulted from the COVID-19 pandemic by ramping up vaccinations since the first quarter of 2021, a third wave of the virus has already taken a toll on the Asia-Pacific region. Before the pandemic, this region enjoyed the steepest human development growth globally. However, the progress is uneven within the region especially in respect to development in health and the access to essential health and medicine. The inequality in health is expected to worsen due to the pandemic largely due to the current unequal distribution of COVID-19 vaccines, between advanced and less-developed countries in Asia-Pacific region. High-income countries have deals securing enough doses to vaccinate their populations twice over, while in many low-income countries fewer than one in 100 people had received a single dose of vaccine.

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)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.324
GPT teacher head0.402
Teacher spread0.077 · 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