Liberalisation of Agricultural Trade - Global Implications and what it Means for the EU
Bibliographic record
Abstract
The liberalisation of agricultural trade is expected to become a key element of the agreement resulting from the WTO's Doha Round. The economic impacts of agricultural trade liberalisation are evaluated in this study using a global numerical general equilibrium model. A broad policy package including the elimination of export subsidies, tariff reductions, and cuts in publicly financed domestic support is evaluated. World trade is expected to expand in liberalised commodities by between 10 and 25 per cent. Growth will be most pronounced in beef and sugar trade. Tariff reductions are the most important factor boosting trade. Middle-income countries, the EU, the transition economies of central Europe and other industrial countries, excluding the USA and Canada, are likely to benefit most from the reform. The efficiency gains as measured by fixed-price GDP will be from 0.1 to 0.3 per cent in these countries. Usually, consumption increases more because of declining food prices in food-importing countries or because of improved terms of trade in food-exporting countries.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".