Bibliographic record
Abstract
Globally, Agriculture it seems is back on the development agenda, seen as a key to spurring growth and reduction poverty, and as a key route to meeting the Millennium Development Goals. Continent -wide policy can safeguard each country’s independence. The main focus of this paper is to analyse global agricultural policies and critically appraisal of their policies and arrive the best policies. The study is based on meta-analysis. The status of global agricultural policies in general and selected continent wise policies in particular is analysed. It also suggests the best future global agricultural policies. World as a whole the pressures on agriculture to produce much less than indicated projections for the period to 2050 because of deceleration population growth. The basic changes in Europe models concerning the transformation from supply driven models of traditional agriculture to the concept of modern agriculture focusing on demand-driven types of market agriculture. The North American Model; United States, Mexico, and Canada have each made significant changes to their agricultural policies over the past several years particularly in the area of income supports. The Latin America continent was confronted with a new twist to the Green Revolution model, with the introduction of genetically modified (GM) crops and run by transnational corporations. In Africa, agriculture is runs by the significance of aid provided by donors. The successful Asian State Green revolution model focuses more on seed and technologies to increase production. The most common policy response taken by the emerging economies – and also worldwide – has been to reduce or suspend import tariffs on food products. The year 2011 highlighted after many years of neglect, agriculture and food security are back on the development and political agendas. The study suggests to focus future policies on agriculture as a global agenda and global efforts.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".