Feeding Minds: Exploring How Adults Shape Children’s Perceptions of Food through Everyday Conversations
Bibliographic record
Abstract
This study explores the dynamics of food-related discourse between adults and children in British families, utilizing the CHILDES (Child Language Data Exchange System) database to analyze naturally occurring conversations. By employing corpus linguistics, natural language processing (NLP), and data science methods, the research examines how food is discussed, identifying patterns in vocabulary use, conversation initiation, and the linguistic framing of eating behaviours, which can exert lasting influences on future wellbeing. The findings reveal that the food-related vocabulary of children and adults mostly overlaps, centring around staple foods. However, there are notable differences: children frequently mention sweet foods and express desires for them, whereas adults use a broader and more sophisticated vocabulary, including terms related to meal preparation and diverse cuisines. Notably, although children place particular emphasis on sweet foods, adults too frequently mention them highlighting that both groups engage with sugary treats, albeit with different discursive functions. Moreover, adults are nearly twice as likely as children to initiate food-related conversations, reinforcing their role as primary facilitators of food discourse. An analysis of the different usages and patterns of the verb eat highlights the instructional and regulatory nature of adult speech, with frequent use of imperatives emphasizing that children must finish their food, and conditional statements linking eating to rewards or consequences. These patterns suggest that adults may shape children’s perceptions of portion sizes, satiety, and mealtime behaviour through epistemic primacy. Additionally, gendered differences in food-related praise reflect broader cultural expectations, with boys receiving more frequent positive reinforcement for eating.
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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 | 0.004 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".