Réductions d’intrants : des changements techniques, et après ? Effets de verrouillage et voies d’évolution à l’échelle du système agri-alimentaire
Bibliographic record
Abstract
This paper analyses how possibilities of input reduction in arable crops, despite scientific evidence, are limited by lock-in effects within the agri-food system, and more specifically by industrial and marketing criteria. We will first demonstrate, on the basis of a path-dependency analysis, how this agri-food system has embraced on the long run the “paradigm of intensification”, due to a convergence of innovations (homologation of new pesticides, selection of cultivars, changes in farming practices, etc.) and of actors strategies, which led to a change from a curative use of pesticides to a more systematic one. From a series of sociological interviews with French cooperatives and millers, we examine their positions in terms of cultivar choice and analyse the limits of this factor of change, when not combined with changes in growing practices. We show that existing quality signs and guidelines in the wheat and milling sector aim at ensuring products traceability and market segmentation much more than at reducing inputs. Finally, the lack of storage, transforming and marketing capacities for new crops to be introduced in arable crop rotations in order to lengthen them points out the importance of governance and organization within the foodchain, for which the cases of alternative systems with less or different intermediaries might offer some new perspectives
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".