Bibliographic record
Abstract
This annual report monitors and evaluates agricultural policies in 54 countries, including the 38 OECD countries, the five non‑OECD EU Member States, and 11 emerging economies. It finds that despite some modest declines in recent years, support to agriculture has remained close to recent historical highs. While changes in support have been limited, agricultural policies have been both reactive and proactive, boosting the sector’s capacity to respond to current challenges while aiming to ensure that food systems are fit for purpose as future conditions evolve. This year’s report focuses on policies fostering sustainable productivity growth in agriculture. Governments are applying a large variety of approaches to improve productivity while preserving natural resources and reducing agricultural greenhouse gas emissions. The report notes, however, that clearly defined targets related to sustainable productivity growth and measurable indicators of progress are important to ensure that policies achieve their stated objectives. The report also notes that making more effective use of producer support to promote innovation and environmental sustainability on the farm, and refocusing overall support towards targeted R&D, can better leverage public spending to deliver public goods and sustainable productivity growth. In line with the 2022 OECD Agriculture Ministerial Declaration, the report identifies a seven-point policy agenda for making agriculture more sustainable, productive and resilient, and for improving the effectiveness and efficiency of agricultural support and markets.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.583 | 0.283 |
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".