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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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 teacher head, 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".