Bibliographic record
Abstract
Abstract Like all species, humans change our environments to get food.Foodgetting is the dimension of human history that links us directly and indirectly with all other beings. Inescapably and at once both historical and natural, human foodgetting can be understood both as natural history and as historical nature. It implicates our species being in the evolving web of life. In its complex embodied, encultured and social relations, human nature evolves. To embrace that recognition requires thorough revision of inherited ideas. I draw on specific contributions among many thinkers engaged in this project by following a foodgetting thread through several literatures: (1) approaches to reconnecting natural with social sciences of human nature; (2) a “deep history” (Shryock and Smail 2011) of agriculture, which connects prehistory to written history, by Mazoyer and Roudart (1997, 2006), and its limits; (3) ecological resilience theory, and its model of panarchy, which resonates with emergence, dissolution, and reconstellation of food regimes and food regime transitions. This sets the stage for (4) clarifying different paths taken by food regime analysts, including my differences with co‐founder Philip McMichael. (5) I conclude by suggesting an approach to intentional change of human institutions centred on emergence, and (6) an example of emerging ways of organising territory centred on foodgetting.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".