Changing the terms of trade policy making: from the ‘club’ to the ‘multistakeholder’ model
Bibliographic record
Abstract
In the light of the events surrounding the Seattle Ministerial in December 1999 and the fate of the Multilateral Agreement on Investment, increasing attention is being paid not only to the substance of trade policy but to the processes through which it is effected. Growing realization of the need to enhance transparency and legitimacy in trade policy decision-making is reflected in debates on the openness of the multilateral processes most obviously represented by the World Trade Organization. Somewhat less attention has been paid to ways in which national trade policy processes are adapting to these pressures. The article argues the need to redress the balance and suggests that it is possible to analyse the development of at least some national trade policy environments in terms of a shift from a ‘club’, through an ‘adaptive club’ to a ‘multistakeholder’ model. These are examined with specific reference to the development of the latter in the Canadian and European Union contexts.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".