Trade Dimensions of Food Security, OECD Food, Agriculture and Fisheries Papers, No. 77, OECD Publishing.
Bibliographic record
Abstract
This report examines the different channels through which trade openness (and reforms to achieve it)\ncan affect a country’s food security. The overall conclusion is that trade openness has a\npositive net\nimpact on food security, although specific constituencies, including some poor households, could see\ntheir immediate food security threatened by the withdrawal of trade protection. The challenge for\npolicymakers is to design flanking policies\nwhich enable countries to reap aggregate gains yet mitigate\nspecific losses. Those policies include social protection and the provision of risk management tools,\nallied with investments in productivity so that average incomes rise to the extent that any a\ndverse shock\nto incomes is unlikely to jeopardise food security. Developing countries are increasingly able to deploy\nsuch targeted instruments. Lessons are also being learned with respect to the political economy of trade\nreform, such that changes can be\nintroduced in a way that minimises adjustment stresses and helps build\nthe consensus needed to lock in the benefits of trade policy reform.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".