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Record W7033546374

The roles of risk perceptions and risk tolerance in consumer valuation of food safety in Canada

2003· dissertation· en· W7033546374 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2003
Typedissertation
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFood safetyRisk perceptionFood safety risk analysisRisk assessmentValuation (finance)PerceptionValue (mathematics)Contingent valuation
DOInot available

Abstract

fetched live from OpenAlex

Food-borne disease imposes significant costs on food marketing and health systems, making food safety a serious, global issue. This study investigates the roles of risk perceptions and risk tolerance in shaping Canadian consumers' willingness-to-pay for food safety risk reductions. Non-hypothetical experimental auctions were used to elicit consumer valuations of food safety improvement. Results suggest that consumers generally overestimate the likelihood of becoming ill due to food-borne disease relative to scientifically-estimated odds, and are willing to pay a positive amount to reduce food-safety risk. Risk reduction valuations increased with higher initial risk, supporting arguments of diminishing marginal value for risk reductions. To investigate the relationship between food safety concern and risk reduction valuations, individual risk perception scores are constructed based on questionnaire responses. Results indicate that willingness-to-pay for improved food safety tends to decrease as individuals become more risk tolerant.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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 designObservational
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

Citations0
Published2003
Admission routes1
Has abstractyes

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