Obtaining consumer perspectives using a citizens’ jury: does the current country of origin labelling in Australia allow for informed food choices?
Bibliographic record
Abstract
BACKGROUND: Contemporary food systems are vast and complex, creating greater distance between consumers and their food. Consequently, consumers are required to put faith in a system of which they have limited knowledge or control. Country of origin labelling (CoOL) is one mechanism that theoretically enables consumer knowledge of provenance of food products. However, this labelling system has recently come under Australian Government review and recommendations for improvements have been proposed. Consumer engagement in this process has been limited. Therefore this study sought to obtain further consumer opinion on the issue of CoOL and to identify the extent to which Australian consumers agree with Australian Government recommendations for improvements. METHODS: A citizens' jury was conducted with a sample of 14 South Australian consumers to explore their perceptions on whether the CoOL system allows them to make informed food choices, as well as what changes (if any) need to be made to enable informed food choices (recommendations). RESULTS: Overall, jurors' perception of usefulness of CoOL, including its ability to enable consumers to make informed food choices, fluctuated throughout the Citizens' Jury. Initially, the majority of the jurors indicated that the labels allowed informed food choice, however by the end of the session the majority disagreed with this statement. Inconsistencies within jurors' opinions were observed, particularly following delivery of information from expert witnesses and jury deliberation. Jurors provided recommendations for changes to be made to CoOL, which were similar to those provided in the Australian Government inquiry. CONCLUSIONS: Consumers in this study engaged with the topical issue of CoOL and provided their opinions. Overall, consumers do not think that the current CoOL system in Australia enables consumers to make informed choices. Recommendations for changes, including increasing the size of the label and the label's font, and standardising its position, were made.
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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 | 0.042 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".