Rewriting Desirability an Exploration Of North American Ugly Food Marketing Campaigns
Bibliographic record
Abstract
The application of aesthetic and cosmetic standards to fresh fruits and vegetables results in the discrimination and elimination of an abundance of “ugly” foods. The systematic elimination of “ugly” foods, which are foods deemed suboptimal in their appearance, weight, shape, or size, greatly contributes to the global problem of food waste in a time of increasing food insecurity. Grocers and food retailers in Canada and the U.S. have begun promoting the sale of “ugly” foods in an attempt to combat the issue of food waste. This MRP (Major Research Paper) examines the names and titles of eleven North American “ugly” food marketing campaigns. This project explores how “ugly” foods are communicated to consumers in North America and how the chosen language used in these campaign titles works to normalize “ugly” foods and attempts to alter their desirability to consumers. The analysis is conducted using textual coding and the Critical Discourse Analysis (CDA) method. Moreover, this MRP reflects on the power of grocers and food retailers to encourage the consumption of “ugly” foods, reduce food waste at the retail level, and effect change in the global food system.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".