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Record W1736372598 · doi:10.15353/cfs-rcea.v2i2.91

CRFA - SYNTHESIS - The role of transnational food and agriculture corporations in creating and responding to food crises

2015· article· en· W1736372598 on OpenAlexaffvenue
Caitlin Michelle Scott

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAgricultureLivelihoodTransparency (behavior)BusinessGlobalizationFood systemsFood sectorCommodityCommerceMarket economyFood securityInternational tradeIndustrial organizationEconomicsPolitical scienceFinance

Abstract

fetched live from OpenAlex

Transnational corporations (TNCs) have been important players in the globalization of food and agriculture. The preceding papers focused on the ways in which the modern food system is a result of the growing influence and global expansion of agrifood TNCs. Pat Mooney outlined the increasing concentration in agricultural input corporations, highlighting the environmental and health costs that result from their power and control. Jennifer Clapp described the latest changes in commodity trading firms, showing that the historically private nature and evolving horizontal and vertical integration in this sector, along with new players, have been damaging for the environment and livelihoods. She argues for increasing transparency and greater regulatory oversight. Finally, Gyorgy Scrinis explored the ways in which food and beverage manufacturing companies (‘Big Food’) are responding to concerns about the health impacts of their products by adopting forms of corporate nutritionism.

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)0.004
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.009
Science and technology studies0.0030.002
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0430.003

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 head0.045
GPT teacher head0.219
Teacher spread0.174 · 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
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

Citations2
Published2015
Admission routes2
Has abstractyes

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