Standards as a commons: Private agri-food standards as governance for the 99 percent
Bibliographic record
Abstract
Private agri-food standards have emerged in response to the constraints imposed on the role of the state under the influence of neoliberalism. These standards reflect the ongoing ‘value wars’ between the money code of value and the life code of value (McMurtry 2002). While some private agri-food standards operate within the money code of value (e.g., Red Tractor or CanadaGap), others can be more fruitfully situated within the life code of value because they ‘remove the veil’ (Hudson and Hudson 2003) from food commodities to reveal the exploitative social, economic and environmental relations inherent in today’s “feral capitalism” (Harvey 2011). This paper will use these codes of value to interpret three cases – organics, fair trade and Local Food Plus – with the aim of informing discussion regarding the emergence of standards as a form of governance. It will argue that conceptualizing standards as a commons will help us to better analyze the threats and opportunities posed by the rise of private agri-food standards and will open up the possibility that they can provide a form of life-protective governance that benefits what has come to be known as ‘the 99 percent.’
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".