Moral foundations and credence attributes in livestock production: Canada
Bibliographic record
Abstract
Purpose The purpose of this study is to assess the differences in individual purchasing and voting decisions for livestock products, produced with lower levels of antibiotic use or higher levels of environmental sustainability, by consumers with different degrees of agreement with moral foundation statements. Design/methodology/approach Data are collected from two national online surveys that were conducted in Canada in 2016 and 2017, including socio-demographic, attitude, belief and stated choice questions. Data are analysed using hierarchical cluster analysis and ordered probit regressions. Findings Respondents who strongly agree with the individualizing moral foundation statements are more likely to buy more environmentally sustainable milk/yogurt and pork from pigs that are raised with reduced antibiotic use, as compared to respondents who have weaker agreement with the statements. Respondents with stronger agreement with the moral foundation statements are also more likely to vote in favour of stricter livestock environmental standards and disease protocols. Research limitations/implications Monitoring people’s moral concerns might help in predicting consumers’ responses to new or different production practices. Originality/value Although moral foundations have been linked to other purchase decisions, in this study, the focus is on specific aspects of environmental sustainability and antibiotic use in livestock production. Both of these challenging issues are controversial and facing either regulatory changes (antibiotic use in livestock) or significant livestock production changes (responding to concerns that livestock production is less sustainable than plant production) in most developed countries. Understanding the linkages between fundamental beliefs and probable consumer behaviour will assist in predicting negative or positive outcomes to the regulatory or industry-based changes to livestock production. Differences between an individual’s desire to be able to identify products with certain attributes for purchase vs an individual’s desire to have government regulate industries to higher standards, in both contexts, will also be linked to the individual’s level of moral foundation beliefs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".