How Does Free Trade Become Institutionalized? An Expected Utility Model of the Chretien Era
Bibliographic record
Abstract
This paper seeks to go beyond the question of 'why free trade?' and pursues issues related to the tendency for controversial free trade agreements to become institutionalized. In other words, why do opponents of free trade not mobilize to overturn it? Even more puzzling, why do opposition parties, which had opposed passage of free trade in the first place, not undo liberalization undertaken by their predecessors upon coming to power? Rather than seek reversal, it is not uncommon for free trade opponents, upon assuming control of the government, to deepen liberalization initiatives, hence serving to institutionalize the very policies they had decried vigorously. Six sections make up this study. It begins with a statement of the basic puzzle and an illustration in the recent Canadian context. The second is a theoretical discussion of opposition parties and free trade. An expected utility model, based on the limits of rent-seeking, is introduced in the third section to explain institutionalized free trade. The fourth section provides the background to the case at hand, that is, the evolution of free trade as a politico-economic issue in Canada. The fifth section applies the expected utility model to the superficially puzzling case of Canadian Prime Minister Jean Chretien's dramatic about-face on the issue of trade liberalization after coming to power. Sixth, and finally, the contributions of the model are reviewed, along with directions for future research.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".