Bibliographic record
Abstract
Every decade or so, food becomes newsworthy globally because of a price spike, either upwards (hurting consumers, as in 1973 and 2008) or downwards (hurting farmers in open economies, as in 1986). Most such price spikes are a consequence of major policy shifts, since local weather-induced supply shocks in a multi-country trading world tend to offset each other. Fluctuations in international food prices are exacerbated by trade restrictions that vary with those prices, and are worst for the most-insulated markets such as rice. Asian rice policies thus contribute to world food price instability. More broadly, however, the gradual reduction in anti-agricultural and anti-trade policies in many Asian emerging economies in the past quarter-century has contributed very substantially to global economic development and poverty alleviation. After examining how large the fluctuations in real international prices for food are relative to those for other primary products, this paper examines the extent of opening up of agricultural markets in Asia relative to other developing economies. It then examines what could be done by governments in Asia and elsewhere to achieve more efficient and equitable outcomes for food markets in the future that are both growth enhancing and poverty alleviating.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".