Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Investing in knowledge to support the adoption of environmentally-friendly farm practices is \ncommonly perceived as a key driver behind innovation processes in agriculture. Yet changes at the \nnational and global levels have led to dramatic changes in the orientation of advisory services, how these \nare organised, and their methods of intervention. This report examines the role, performance and impact \nof farm advisory services, as well as the training and extension initiatives undertaken in the OECD area to \nfoster green growth in agriculture. The merits of the different types of providers are also discussed and \nthe experience of selected OECD countries presented. \nAssessing the impact of agricultural advisory services, training and extension measures on green \ngrowth involves a range of methodological issues, but for which evaluations of outcomes and assessment \nof their overall cost-effectiveness is scarce. Nevertheless, a key conclusion of this report is that there is no \none-size-fits-all evaluation methodology and that any evaluation of the impact of these measures should \ntake into account all actors that provide agricultural advisory services, training, and extension measures as \nthey are part of a wider agricultural knowledge and innovation system in which multiple stakeholders \ninteract. \nThis report contributes to OECD work on green growth which emphasises the importance of research, \ndevelopment, innovation, education, extension services and information to increase productivity in a \nsustainable way. This report was prepared by the OECD’s Trade and Agriculture Directorate and was \ndeclassified by the OECD Joint Working Party on Agriculture and the Environment in January 2015. \nDimitris Diakosavvas was project leader and is the principal author of this report. Chapter 5 draws on \nbackground papers prepared by consultants for the five case studies: Bruce Kefford and Clive Noble \n(Australia); Rivellie Tschuisseu and Pierre Labarthe (Canada), Janet Dwyer and Matt Reed (England and \nWales), Dimitris Damianos (Greece) and Brian Bell and Michael Yap (New Zealand). A further paper \nprepared by Clunie Keenleyside also contributed to the present report. Comments and review from OECD \ncolleagues are also appreciated and acknowledged, including Nathalie Girouard, Justine Garrett and \nAnnabelle Mourougane. Françoise Bénicourt and Theresa Poincet provided invaluable secretarial \nassistance throughout the production process. The report was prepared for publication by \nMichèle Patterson, who also co-ordinated its production.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle