Reconquer and divide: comparative standard-setting strategies among producer organizations
Bibliographic record
Abstract
Abstract Food standards, which are used to signal adherence to sustainability goals or a specific origin, have deep political implications. Standards crafted by retailers, processors, or third-party actors such as non-governmental organizations (NGOs) often disempower farmers. Moreover, due to the liberalization and globalization of many food value chains, producer organizations (POs) lost some of their legal privileges and market protections. This paper analyzes how POs in the Global North sought to regain their control over food markets by establishing their own standards. These strategies and their consequences are considered across three dimensions: the internal life of the PO, the relevant market institutions, and the relationship between the PO and the state. Our case studies (N = 5) performed in France and in Québec, a French-speaking province of Canada, span across a variety of food sectors. Drawing on qualitative material, we designed our explanatory framework through an abductive, iterative method. Although standards crafted by POs have, in some cases, reshaped market institutions to their advantage and have repositioned them in the governance of food markets, they come at a cost. They may create tensions within POs and clash with the agrarian values of solidarity, democracy, and autonomy. Overall, this article challenges the assumption that food standards are mainly governed by private actors and sheds light on the new alliances and new identities of POs.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".