What's on the Menu for an Equitable Approach to Nutrition Labelling in Restaurants?
Bibliographic record
Abstract
The primary aim of menu labelling should be understood as informing consumers such that they are better able to make informed food purchasing and consumption decisions; the extent to which consumers’ behaviours or, indeed, health outcomes, are affected may be contingent on several other factors and should therefore be considered more distal aims of what menu labelling intends to, or is able to, achieve. It is of importance to be clear about the nature and scope of menu labelling, including what it might reasonably be expected to achieve, in order to elucidate the morally relevant equity considerations that ought to accompany the design and implementation of such interventions. This commentary attempts to begin to specify what these equity considerations ought to look like given the specific situational and dispositional factors associated with menu labelling. It concludes that the goals of menu labelling interventions should be, at a minimum, to strive to give consumers equality of access to nutrition information and/or the equal opportunity or capability to make informed food decisions in the eating out environment. These considerations support a universal approach to menu labelling, but may also require targeted strategies to attend to the needs of those less capable of making informed food decisions.
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.
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 | 0.034 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".