THE EFFECTIVENESS OF THE CANADIAN ANTIDUMPING REGIME: IS TRADE RESTRICTED?
Bibliographic record
Abstract
Canada has the oldest antidumping (AD) regime in the world and has to this day been one of the heavier users of the measure. It is an important trade policy that affects a relatively large proportion of Canadian imports, and it requires a better understanding of its use and its effects on trade. In 2003, there were 92 measures in place affecting a sizeable part of total Canadian imports (1,231 million dollars). The obvious question is whether imposing antidumping duties actually protects the domestic industry. We look at the trade effects of antidumping policy in the manufacturing industry in Canada. We also look at the effect of an antidumping action on the level of imports from the non-named countries, to analyze the extent of trade diversion. It is possible that imports might be diverted away from the alleged source country to the non-alleged countries rendering antidumping law ineffective in benefiting the domestic industry. We use AD data for the years 1990-2000, and import data disaggregated at the 10 digit HS level and we find that overall, Canadian antidumping is an efficient tool for restricting imports from the countries that are named or alleged to be dumping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".