Bibliographic record
Abstract
The previous chapter outlined how the EU regime inherited from the national era was made consonant with the imperatives of the contemporary trade environment but with significant concessions that left its key protective measures intact if not inviolate. This chapter turns our attention across the Atlantic and moves to examine the evolution of the national regime in the US. Out of the three economies in North America, focus on the US is apposite here both because of its market importance (in 2006/07 the USA consumed 9.23mt of sugar, Mexico 4.98mt and Canada 1.43mt) and its political influence (ISO 2008: 15). As outlined in Chapter 4, the type of agricultural support adopted in the US was of crucial significance to the rules adopted within the trading system at large. This remains the case today. Through its position as the biggest agricultural exporter and its material and institutional power in the global trade architecture, the policy vision emanating from the US has had direct consequences for global agricultural trade. Within this vision, sugar has been a notable exception to the general thrust of aggressive market opening. During the Uruguay Round for instance, since the US had lowered its average trade distorting support under the 1990 Farm Bill and because tariffication and minimum market access provisions in sugar had already been set in place, there was no need for adjustment, and certainly no effort made to make this otherwise. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".