Survey Reveals Urban Consumers' Attitudes and Beliefs Regarding the Use of Wax on Apples
Bibliographic record
Abstract
Abstract This research explored consumers' awareness, attitudes and beliefs regarding the use of wax on apples, using a survey consisting of five demographic questions, 14 apple wax questions and five apple wax information statements. Consumers responded to queries regarding the use and nature of wax coatings, as well as any health and environmental concerns. They identified their preferred apple treatment (unwaxed, waxed, either) four times throughout the survey, after being presented with information. Consumers' responses were evaluated according to their demographics (age, gender and ethnicity). Statistical analyses (frequency plots, analysis of variance) were used to evaluate data from the two largest ethnic subgroups of consumers (European, Asian) (n = 781). On average, consumers lacked knowledge and information about apple waxing. Interestingly, 84% of consumers initially stated they preferred unwaxed apples. While some 40% of consumers changed their preferences once additional information was provided, another 42.3% of consumers did not. Practical Applications This research successfully documented the attitudes and beliefs of urban consumers toward the use of wax on apples. It suggested some consumers might be more willing to accept waxed apples, if additional information (brochure or leaflet) was available, while others would not. As such, the research provided industry with objective information to assess the appropriateness of their marketing/retailing practices in order to meet the needs of their consumers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".