Comparing the ways a sample of Brazilian adults classify food with the NOVA food classification: An exploratory insight
Bibliographic record
Abstract
The aim of this study is to have an exploratory insight on how a sample of Brazilian adults classify food, attempting to identify the main factors involved in this process, and to compare these classifications to the NOVA food classification of the 2014 Brazilian dietary guidelines. An exploratory and qualitative approach was conducted with a selected sample of teachers, administrative technicians, and students (N = 24) from the Federal University of Grande Dourados, Brazil. First, using the pile sort method, participants were asked to freely classify 24 pictures of food (sourced from examples of the four food groups specified in NOVA) into food groups meaningful to them. Next, in semi-structured interviews, participants were asked to describe the food groups they created. The food groups created by participants were analyzed using non-metric multidimensional scaling followed by hierarchical cluster analysis, and the interviews were analyzed using content analysis. Participants had a mean age of 30 (±9.4) years. A total of 128 food groups were created by 24 participants (an average of five food groups per person); and a total of 55 non-mutually exclusive groups names were used by them to describe these food groups. Sixteen themes emerged from the content analysis. The most recurrent themes were food groups, nutrients, foods I consume, foods I do not consume, and food processing. Contrasting themes such as real food and junk foods, meals and ready-made foods, healthy foods and unhealthy foods were also noted. Six clusters emerged from the cluster analysis, each related to one or more themes. Overall, a striking similarity was observed between the ways the individuals classified food and the NOVA food classification.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".