Bibliographic record
Abstract
Trust and perceptions of fairness in markets have been shown to be important in consumer behavior in different contexts. However, there have not been many studies relating the concept of fairness is supply chains to food purchasing behavior. In this study, we explore the relationships between trust, fairness and perception of quality of food produced from three food technologies. The technologies are as follows: (i) bread fortified with omega-3 fatty acids using nanotechnology (ii) pork chops from pigs selectively bred for disease resistance using genomic selection (iii) baby spinach treated with essential oils to reduce concentrations of E. coli O157:H7. Data are from a small exploratory project conducted in 2015 at the University of Alberta, Canada, where 31 non-academic staff participated in stated preference experiments and completed a survey questionnaire. Stated preference data are analysed using conditional logit regressions. Different potential explanatory fairness variables are created using questions from previous studies. From the results, both the constructs associated with trust and with fairness in supply chains have explanatory power. Although there are some variations in results (depending on the type of questions used to measure fairness), fairness positively influences trust in the food supply chain. Future studies might need to consider including perceptions of fairness in supply chains in the analysis of consumer acceptance of novel technologies.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".