Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".