Bibliographic record
Abstract
Moving towards a more sustainable, healthier, and equitable food future requires a significant system transformation. Policies to achieve this transformation are notoriously difficult to achieve, especially where actors with conflicts of interest are involved in governance. In this paper, I analyze how corporate actors frame issues inside a process to develop Front-of-Pack Labelling across the Caribbean. Focusing on three major framing strategies, I show how industry actors argued 1) (falsely) that FOPL would privilege Chilean food suppliers; 2) that FOPL would constitute a major transgression of international trade law; and 3) that a regional public health organization (the Pan-American Health Organization) is an illegitimate influence on the policy. Together, these three framing strategies reconstruct the policy problem as one of trade rather than public health. I argue that the resulting narrative is both a product and a function of the discursive power food companies wield in the standard-setting process and provide empirical detail about how food companies act to prevent policy attempts facilitating food systems transformation.
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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".