Le crowdfunding maraîcher en Normandie. Levier de transition d’agrosystèmes productivo-résidentiels
Bibliographic record
Abstract
L’usage croissant des technologies de l’information et de la communication accompagne les mutations du financement des filières agrialimentaires. L’essor du crowdfunding contribue à pallier le difficile accès au crédit bancaire pour certaines petites exploitations peu conventionnelles. En s’appuyant sur une méthode mixte appliquée au maraîchage en Normandie, l’article montre en quoi ces plateformes numériques appuient la transition agroécologique et, révélant le capital fixe et les intrants des petites exploitations maraîchères alternatives, quelle transition est ainsi financée. En outre, les données liées au financement participatif offrent des résultats-hypothèses, à l’échelle territoriale, quant aux types d’agrosystème plus ou moins propices au financement participatif.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".