Place-Based Food Systems: Making the Case, Making it Happen
Bibliographic record
Abstract
First paragraph: In less than a century, our food system has been transformed into a complex network of global-industrial supply chains, increasingly disconnecting us from the people and processes that provide our food. Such a ‘market-driven’ system externalizes many of its social, environmental, and economic costs. At the same time, it concentrates power and profits among a few stakeholders who maintain hegemonic control of the food systems, yet are often far removed from its negative impacts. The list of transgressions is long and familiar to us: extensive environmental degradation, unjust labor conditions for food workers, the collapse of farming communities, epidemic occurrence of western diet–related disease, biodiversity loss, and on it goes. It is a system that produces more food than at any period in history—more than enough to feed the global population (Holt-Giménez, Shattuck, Altieri, Herren, & Gliessman, 2012, Food and Agriculture Organization of the United Nations [FAO], 2017)—yet leaves more than one in 10 people experiencing hunger (Food and Agriculture Organization of the United Nations [FAO], International Fund for Agriculture Development [IFAD], UNICEF, World Food Programme [WFP], & World Health Organization [WHO], 2019).
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".