Bibliographic record
Abstract
Canada has the oldest antidumping (AD) regime in the world and has to this day been counted among the main users of AD measures. It is an important trade-remedy instrument that affects a relatively large proportion of Canadian imports, although its use and effects on trade could still be better understood. For example, in 2003 there were 92 measures in place affecting around C$1.3 billion worth of Canadian imports. An important question is whether imposing AD duties actually protects the domestic industry from import competition. We analyze the trade effects of AD policy in the manufacturing industry in Canada. We also look at the effect of an AD action on the level of imports from countries not named in the analysis in order to examine the extent of trade diversion. It is possible that imports might be partly diverted away from the alleged source country and to non-alleged countries, rendering AD laws ineffective in terms of benefiting the domestic industry. We construct a database using AD data for the years 1990–2000, and import data disaggregated at the ten-digit Harmonized System (HS) level, and ultimately find that Canadian AD policy is an efficient tool for restricting imports from countries that are “named” in an investigation or alleged to be dumping. When adopting a relatively coarse classification of named cases into two groups—affirmative (affirmative AD decisions and price undertakings) and negative (negative AD decisions)—we also find some evidence of trade diversion and “harassment” effects of AD.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".