What I say isn’t always what I do: Investigating differences in children’s reported and actual snack food preferences
Bibliographic record
Abstract
The current study sought to explore discrepancies between children’s stated snack food motivations and actual food choices, using the Implicit Association Test (IAT) as a measure of implicit attitudes towards ‘healthy’ and ‘unhealthy’ foods. Participants were children aged 6-12 years (n=118), from two primary schools on the South Coast of NSW, Australia – a public school in a semi-rural suburb south of a sea-side city and a public school in a largely residential northern suburb of the same city. The children completed a questionnaire about motivations for snack choices, participated in an activity, completed two further questionnaires, selected snack foods from an in-class store, and participated in two rounds of an IAT ‘game’ pairing pictures of snack foods with positive and negative words. As hypothesized, the majority of children reported ‘healthiness’ as their primary motivator for snack food choice, but when faced with an actual purchase decision predominantly chose unhealthy snacks. It appears that children may have internalized the ‘moral’ values attributed to healthy and unhealthy foods and that this process influences both their explicit and implicit attitudes. However, their actual food choices are likely to be influenced by other factors, and thus more complex to understand and influence.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".