Non-market food production can contribute to diverse dimensions of food security according to key informants in northern New England
Bibliographic record
Abstract
Recent studies have found that growing or wild-harvesting some of one’s own food is associated with food security in high-income societies. Yet this research has not established causal relationships, and it measures household food security using indicators that assess only access to market food. To disentangle how non-market food production interacts with food security, we interviewed 26 key informants who play central roles in communities of gardeners, hunters, fishers, foragers, and homesteaders in northern New England, U.S.A. These informants indicated that non-market food production relates ambiguously to short-term food access in high-income societies where market food is cheap relative to wages. But non-market production can enhance all other recognized dimensions of food security: availability, adequacy, acceptability, agency, utilization, stability, and sustainability. Causation can run the other way, too: food insecurity was said to increase the likelihood and intensity of engagement in non-market food production. Yet poverty can deprive food-insecure households of the equipment, money, skills, and land access needed for successfully producing their own food. Overall, our informants portrayed non-market food production as a skills-based safety net for reliably feeding oneself from the landscape through personal and societal crises, from the distant past to the climate-change future.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".