MétaCan
Menu
Back to cohort
Record W2966682993 · doi:10.1108/bfj-10-2018-0708

From inhumane to enticing: reimagining scandalous meat

2019· article· en· W2966682993 on OpenAlexaff
Kristie O’Neill

Bibliographic record

VenueBritish Food Journal · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsOriginalityPopularityNewspaperContext (archaeology)Value (mathematics)Transformative learningGuardianAdvertisingSociologyHistoryPsychologyPolitical scienceBusinessComputer scienceSocial scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to understand how the meanings of veal change from 1989 to 2014 in the pages of two major newspapers. Design/methodology/approach Articles in The Guardian and The Daily Telegraph that use the word “veal” were selected ( n =1,387). Articles were read for emergent themes and each use of the word veal was coded. Each newspaper had phases of popularity in the use of the word “veal,” and unique words for each of these phases were identified. The context of these unique words was examined in order to illustrate changes in what to eat and why, as well as how to access food and act toward it. Findings This paper illustrates how readers are meaningfully encouraged to engage in food politics in ways that may be incrementally transformative, but do not involve demanding food as a right. Originality/value This paper illustrates that normalizing scandalous food involves complexity and subtle changes. Shifts in messages are detected and analyzed using the related concepts of subsistence standards and practices of reciprocity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.998

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.183
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 teacher head, not a consensus.

Study designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBritish Food JournalSame topicOrganic Food and AgricultureFrench-language works237,207