Trade Liberalization and Rural Poverty (Thomas Hertel, Purdue University and Terry Sicular, University of Western Ontario, organizers) AGRICULTURAL TRADE LIBERALIZATION AND POVERTY DYNAMICS IN THREE DEVELOPING COUNTRIES
Bibliographic record
Abstract
Many developing countries implemented sweeping agricultural reforms over the last decade. Reforms have included the removal of quotas and price controls, changes in in-ternational trade barriers, and the commer-cialization and privatization of state market-ing boards for key crops. These reforms have often generated intense criticism from groups claiming that they hurt poor farmers and poor households. This concern has generated an ex-tensive literature on the economics of agri-cultural trade reform in developing countries, much of which has focused on explaining the large variations in supply response across countries, regions and households (e.g., Key, Sadoulet, and De Janvry). In addition, a num-ber of papers have attempted to simulate the impact on poverty using household survey data and actual or predicted price changes (e.g., Chen and Ravallion). However, in many cases the true impact of agricultural reform is dif-ficult to determine. In part this is because the analysis is based upon household surveys at a single point in time, so that the final (post-adjustment) consequences upon individ-
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".