MétaCan
Menu
Back to cohort

Food crime in the context of cheap capitalism

2018· book-chapter· en· W2913705683 on OpenAlexaboutno aff
Joseph Yaw Asomah, Hong‐Ming Cheng

Bibliographic record

VenuePolicy Press eBooks · 2018
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
Fundersnot available
KeywordsCapitalismContext (archaeology)GlobalizationState (computer science)Political economyPolitical scienceEconomic systemBusinessEconomicsMarket economyPoliticsGeographyLaw

Abstract

fetched live from OpenAlex

The production and sale of unsafe food, which typifies the concept of cheap capitalism, has become a global concern due to the increasing integration and interdependence of contemporary societies. Using secondary data sources, including the media, regulatory bodies, interest groups, and scholarly literature, this chapter explores unsafe food within the conceptual framework of cheap capitalism. By examining the nature and scale of unsafe food, it first argues that cheap capitalism is rampant, posing a greater risk to public health locally and internationally. Second, it argues that the state, the industry, and the processes of globalisation typically constitute the dominant factors shaping and driving cheap capitalism in the food sector. Third, it argues that unsafe food in the Canadian context can properly be understood within the global context of cheap capitalism. Finally, it explores steps being taken to address cheap capitalism in the food industry.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.011
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.060
GPT teacher head0.304
Teacher spread0.244 · 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 designTheoretical or conceptual
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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePolicy Press eBooksSame topicIdentification and Quantification in FoodFrench-language works237,207