Le Conseil économique et l’impact régional de la politique commerciale
Bibliographic record
Abstract
Given that there are a number of possible models of the regional impact of a tariff, one would have hoped that the Council would have attempted to test the standard ones and/or to have developed new and better ones. After almost forty years Mackintosh's model is still probably the most persuasive model of the long run impact of the tariff in Canada. The Council in its main report has been largely content to repeat and to some extent to confuse elements of the conventional wisdom on the subject. Interesting points have been made in some of the background studies, particularly, among the studies reviewed, by Postner and by Dauphin. At a general level, the Council has failed to integrate its recommendations concerning tariff policy into the general framework of regional policy in this country. Specifically, the Council fails to consider explicitly that on "second best" grounds the elimination of tariffs may not lead to an improvement in resource allocation, nor does it consider in any detail policies which would be preferable to tariffs to achieve regional (and other) objectives which require intervention by the government. For a study which suggests that free trade would bring gains of at least five per cent of GNP, or over $8 billion per year at current levels of production, it would be unfortunate if a certain naïveté in exposition of the free trade case were to consign the document to the political dust-bin.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".