Bibliographic record
Abstract
Since the summer of 2007, U.S. food price inflation has increased dramatically. Given public anxiety over fast‐rising food prices, this study attempts to analyze the effects of market factors—prices of energy and agricultural commodities and exchange rate—on U.S. food prices using a cointegration analysis. Results show that the agricultural commodity price and exchange rate play the key roles in determining the short‐ and long‐run movement of U.S. food prices. It is also found that in recent years, energy price has been a significant factor affecting U.S. food prices in the long run, but has little effect in the short run. This implies the strong linkage between energy and agricultural markets in the long run over the recent years. Depuis l’été 2007, l’inflation des prix des aliments aux États‐Unis a augmenté considérablement. En raison de l’anxiété que la hausse rapide des prix des aliments suscite au sein de la population, nous avons tenté d’évaluer, à l’aide d’une analyse de co‐intégration, les répercussions de certains facteurs de marché– le prix de l’énergie, le prix des produits agricoles primaires et le taux change – sur les prix des aliments aux États‐Unis. Les résultats ont montré que les prix des produits agricoles primaires et le taux de change jouent un rôle important dans la détermination des tendances à court et à long terme des prix des aliments aux États‐Unis. Nous avons également constaté que, au cours des dernières années, le prix de l’énergie a eu une forte influence sur les prix des aliments aux États‐Unis à long terme, mais peu d’influence à court terme. Cette observation montre l’étroit lien à long terme des marchés de l’énergie et des produits agricoles au cours des dernières années.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".