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Record W3177987671 · doi:10.1136/bmjgh-2021-006569

Equity and expertise in the UN Food Systems Summit

2021· article· en· W3177987671 on OpenAlexaff
Nicholas Nisbett, Sharon Friel, Richmond Aryeetey, Fábio da Silva Gomes, Jody Harris, Kathryn Backholer, Phillip Baker, Valarie Blue Bird Jernigan, Sirinya Phulkerd

Bibliographic record

VenueBMJ Global Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsInnovation Cluster (Canada)
FundersNational Institute on Minority Health and Health DisparitiesNational Heart Foundation of AustraliaUK Research and InnovationNational Heart, Lung, and Blood InstituteBill and Melinda Gates Foundation
KeywordsSummitEquity (law)LegitimacyTransparency (behavior)Political scienceSustainable developmentIntergenerational equityCivil societyScope (computer science)Human rightsPublic administrationBusinessLaw and economicsSustainabilityEconomicsLawComputer sciencePoliticsGeography

Abstract

fetched live from OpenAlex

The UN Food Systems Summit is expected to launch bold new actions, solutions and strategies to deliver progress on all 17 sustainable development goals (SDGs), each of which requires a transformation in the way the world produces, consumes and thinks about food. However, the summit preparations have started controversially, with claims of corporate capture by prominent civil society groups, who, alongside the current and two former UN Special Rapporteurs on the Right to Food,2 have also noted insufficient attention paid to human rights and to rebalancing power in the food system itself. The issue of corporate capture is an important one for the summit. Early decisions to implement a clear set of rules on corporate
\nparticipation and transparency were missed and need rectifying urgently for the summit to continue with any legitimacy, as the UN
\nSpecial Rapporteurs and the scientists of a new boycott have pointed out. The summit has embraced the (contested, some would argue failed) principle of ‘multistakeholder inclusivity’ as essential for the summit to be a ‘safe space’ for all actors, but with little regards to how power asymmetries between
\nstakeholders within the summit itself must be acknowledged, addressed and accounted for transparently; not a helpful precedent for a global architecture to address those same
\npower asymmetries.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.289
GPT teacher head0.556
Teacher spread0.267 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations36
Published2021
Admission routes1
Has abstractyes

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