How does food get to the table? Sustainable development leaders' framing of food processing in British Columbia
Bibliographic record
Abstract
People need to eat. Because food is essential to well-being, food systems have become a vital component of sustainable development. With the role of converting farm and seafood harvests into the food that graces our tables, food processors are key food systems actors. In addition, food processors are major economic contributors and their operations have huge environmental impact. Despite being a major contributor to social, economic, and environmental well-being; the three main pillars of sustainable development, food processing has largely been excluded from the discourse. In order to understand how leaders in sustainable development think food gets to the table, this study sought to identify how they framed food processing. Frame analysis of the rhetoric and reasoning of sustainable development leaders revealed nine dominant frames that guide leaders thinking about food processing: The Modernization Frame; the Undermining of Foundations frame; the Frankenstein frame; the Cook and the Store frame; the Consumer Stance frame; the Personal Health as Good Individual Food Choices frame; the Fantasy Food System frame; the Silo, Not System Thinking frame; and the Invisible Link frame. These frames revealed negative perceptions and the invisibility of the BC food processing industry explaining its omission from discourse surrounding sustainable development. In addition to facilitating the identification of frames, an invitational rhetoric approach offered the opportunity to test reframes that provided new information about the food processing industry. The results of this unique study provide valuable insights as to how food processing should be reframed in future public relations strategies.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".