International Agricultural Trade Disputes
Bibliographic record
Abstract
Trade disputes between the United States, Canada, and Mexico surrounding agricultural products are widespread and show no signs of abating. As the United States increases agricultural imports while straining under a stagnant level of exports, there is growing tension between trading partners as evidenced by the significant increase in trade remedies being sought by competing countries. International Agricultural Trade Disputes: Case Studies in North America analyzes trade disputes and relevant trade issues from 1995 to 2003. Using case studies to illustrate the complexity of trade disputes, this book examines many factors, such as United States farm policy, the role of politics, and the various trade remedy measures employed in resolving these disputes. With Contributions By: Flynn J. Adcock Janaki R.R. Alavalapati Mel Annand Richard R. Barichello Peter Berck Colin A. Carter W. Hartley Furtan Carol Goodloe Caroline Gunning-Trant Cathy Jabara Walter J. Keithly Won W. Koo Shiv Mehrotra Charles B. Moss Al Mussell David Orden Mechel S. Paggi Warren Payne Stephen J. Powell Robert F. Romain C. Parr Rosson, III Andrew Schmitz Troy G. Schmitz James L. Seale, Jr. Thomas Spreen Ihn H. Uhm Sal Versaggi Michael Wohlgenant Fumiko Yamazaki
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".