Framing Good Food: Communicating Value of Community Food Initiatives in the Midst of a Food Crisis
Bibliographic record
Abstract
Community-embedded food initiatives exist in market economies, but make more-than-market contributions. They challenge the dominant, industrialized food system, while generating non-monetary benefits in their communities. Yet food policy, regulation, and public spending in much of the world is still framed by the values of market economy. Revenue, yield, and technological advancements remain key formal measurements of the wellbeing of food systems. Community-embedded food initiatives like small local businesses and non-profit organizations, are often committed to advancing social and environmental benefits of non-industrialized food, and they call for clearer recognition of their more-than-market contribution to community wellbeing. The Nourishing Communities network has worked with such initiatives for more than a decade, undertaking community-engaged research with practitioners across sectors. The network has found that these initiatives are impeded by a communication conundrum. On the one hand, they are expected (by funders, governments, and other institutions) to demonstrate their value using market-economy measurements and translating what they do into “social returns on investment.” On the other hand, many of those initiatives need non-market terminology to express the values that they espouse and generate. To balance these needs, Gibson-Graham’s framing of “diverse economies” can potentially offer a pathway to better communication and thus more accurate valuing of the work of such initiatives. Their notion of diverse economies offers endless opportunities to frame community food work as valuable in ways that go beyond market-economy measurements. As such, the diverse economies framing offers new possibilities for alternative food, and for more general discussions of social reform.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.026 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".