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Record W4230579198 · doi:10.1017/cbo9781316459812

State Food Crimes

2016· book· en· W4230579198 on OpenAlexaffabout
Rhoda E. Howard-Hassmann

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsRight to foodHuman rightsPolitical scienceSanctionsStarvationState (computer science)DemocracyPoliticsChinaFood securityLawDevelopment economicsGeographyEconomicsAgricultureMedicine

Abstract

fetched live from OpenAlex

Some states deny their own citizens one of the most fundamental human rights: the right to food. Rhoda E. Howard-Hassmann, a leading scholar of human rights, discusses state food crimes, demonstrating how governments have introduced policies that cause malnutrition or starvation among their citizens and others for whom they are responsible. The book introduces the right to food and discusses historical cases (communist famines in Ukraine, China and Cambodia, and neglect of starvation by democratic states in Ireland, Germany and Canada). It then moves to a detailed discussion of four contemporary cases: starvation in North Korea, and malnutrition in Zimbabwe, Venezuela, and the West Bank and Gaza. These cases are then used to analyse international human rights law, sanctions and food aid, and civil and political rights as they pertain to the right to food. The book concludes by considering the need for a new international treaty on the right to food.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0280.007

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 head0.032
GPT teacher head0.232
Teacher spread0.200 · 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
GenreOther

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

Citations37
Published2016
Admission routes2
Has abstractyes

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