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Record W2146115122 · doi:10.1017/s0892679413000191

How We Count Hunger Matters

2013· article· en· W2146115122 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

VenueEthics & International Affairs · 2013
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGlobeAgricultureFamineFood pricesAgency (philosophy)Food insecurityFood securityFood supplyDevelopment economicsAgricultural economicsEconomic growthGeographyPolitical scienceEconomicsSociologySocial science

Abstract

fetched live from OpenAlex

Hunger continues to be one of humanity's greatest challenges despite the existence of a more-than-adequate global food supply equal to 2,800 kilocalories for every person every day. In measuring progress, policy-makers and concerned citizens across the globe rely on information supplied by the Food and Agriculture Organization (FAO), an agency of the United Nations. In 2010 the FAO reported that in the wake of the 2007–2008 food-price spikes and global economic crisis, the number of people experiencing hunger worldwide since 2005–2007 had increased by 150 million, rising above 1 billion in 2009. However, in its State of Food Insecurity in the World 2012 (SOFI 12) the FAO presented new estimates, having revamped its methods and reinterpreted its hunger data back to 1990. The revised numbers for the period 1990–1992 to 2010–2012 reverse the trend to a steadily falling one. Based on the FAO's new calculations, extreme undernourishment peaked in 1990 at a record-breaking one billion, followed by a significant decline through 2006, when progress stalled but did not reverse (see chart below).

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0080.006

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 head0.191
GPT teacher head0.456
Teacher spread0.265 · 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