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Record W2019390163 · doi:10.1080/19320240903346448

Places to Intervene to Make Complex Food Systems More Healthy, Green, Fair, and Affordable

2009· article· en· W2019390163 on OpenAlex
Luvdeep Malhi, Özge Karanfil, Tommy Donald Noel Merth, Molly A. Acheson, Amanda Palmer, Diane T. Finegood

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

VenueJournal of Hunger & Environmental Nutrition · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFood systemsBusinessHealthy foodSustainable developmentEnvironmental economicsMarketingPublic relationsPolitical scienceFood securityEconomicsAgricultureFood science

Abstract

fetched live from OpenAlex

A Food Systems and Public Health conference was convened in April 2009 to consider research supporting food systems that are healthy, green, fair, and affordable. We used a complex systems framework to examine the contents of background material provided to conference participants. Application of our intervention-level framework (paradigm, goals, system structure, feedback and delays, structural elements) enabled comparison of the conference themes of healthy, green, fair, and affordable. At the level of system structure suggested actions to achieve these goals are fairly compatible, including broad public discussion and implementation of policies and programs that support sustainable food production and distribution. At the level of paradigm and goals, the challenge of making healthy and green food affordable becomes apparent as some actions may be in conflict. Systems thinking can provide insight into the challenges and opportunities to act to make the food supply more healthy, green, fair, and affordable.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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