Bibliographic record
Abstract
Abstract Recent years have seen widespread calls to transform food systems to address complex demands such as feeding a growing global population while reducing environmental impacts. But what is a food system and how can we most effectively work to change it? “Food System” can be found describing more limited dietary regimens as well as sector-specific supply chains going back to the 1930s, but its use to describe very large, dynamic, coupled socio-ecological systems gained traction in academic and civil society publications in the 1990s and this use of the term has increased dramatically in recent years. When the influential food system actors from non-governmental organizations, foundations, consultancies, and the UN that this research focuses on talk about food systems, they seem to be talking about the same thing. Yet the interpretive flexibility of the concept obfuscates that people may have very different framings that may be deeply incompatible. Drawing from interviews, participant observation, and document analysis, this paper examines what food systems thinking does in terms of setting the stage for how we enact the food system and efforts to intervene in it. It reveals that rather than leading to more expansive understanding, the unexamined use of the concept food system might actually serve to sharpen divides.
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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.050 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".