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Traceability in the Canadian Red Meat Sector: Do Consumers Care?

2005· article· en· W2137129451 on OpenAlex

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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Supply Chain Traceability
Canadian institutionsMount Allison UniversityUniversity of Saskatchewan
FundersAgriculture and Agri-Food CanadaCooperative State Research, Education, and Extension ServiceUtah Agricultural Experiment StationU.S. Department of Agriculture
KeywordsTraceabilityBusinessFood safetyQuality (philosophy)Value (mathematics)Quality assuranceAgricultureIncentiveMarketingCommon value auctionRisk analysis (engineering)EconomicsEngineeringComputer scienceFood scienceService (business)

Abstract

fetched live from OpenAlex

Increased traceability of food and food ingredients through the agri‐food chain has featured in recent industry initiatives in the Canadian livestock sector and is an important facet of the new Canadian Agricultural Policy Framework (APF). While traceability is usually implicitly associated with ensuring food safety and delivering quality assurances, there has been very little economic analysis of the functions of traceability systems and the value that consumers place on traceability assurances. This paper examines the economic incentives for implementing traceability systems in the meat and livestock sector. Experimental auctions are used to assess the willingness to pay of Canadian consumers for a traceability assurance, a food safety assurance, and an on‐farm production method assurance for beef and pork products. Results from these laboratory market experiments provide insights into the relative value for Canadian consumers of traceability and quality assurances. Traceability, in the absence of quality verification, is of limited value to individual consumers. Bundling traceability with quality assurances has the potential to deliver more value.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.168
Teacher spread0.144 · 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