Design and Evaluation of Technologies for Informed Food Choices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technology increasingly mediates our everyday interactions with food, ranging from its production and handling to the experience of preparing and eating it with friends and family. However, it is unclear whether these technologies support decisions conducive to a healthy diet. In this work, we devised the first heuristics for evaluating a technology’s support for food literacy: the interconnected combination of awareness, knowledge, and skills to empower individuals to make informed food choices. We applied an iterative, expert-driven process to derive and refine our heuristics, starting with an established food literacy framework. We then conducted evaluations with Nutrition and HCI experts to show how the heuristics support the summative and formative design and evaluations of food-related technologies. We show that the heuristics are valuable design tools and that they help participants reflect on food literacy challenges. We also discuss tensions between nutrition and HCI best practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it