The Long and Short of the Canadian-US Free Trade Agreement
Bibliographic record
Abstract
... This paper uses the 1989-96 Canadian FTA experience to examine the short-run adjustment costs and long-run efficiency gains that flow from trade liberalization. For industries subject to large tariff cuts (these are typically low-end manufacturing industries), the short-run costs included a 15% decline in employment and about a 10% decline in both output and the number of plants. Balanced against these large short-run adjustment costs were long-run labour productivity gains of 17% or a spectacular 1.0% per year. Although good capital stock and plant-level data are lacking, an attempt is made to identify the sources of FTA-induced labour productivity growth. Surprisingly, this growth is not due to rising output per plant, increased investment, or market share shifts to high-productivity plants. Instead, half of the 17% labour productivity growth appears due to favourable plant turnover (entry and exit) and rising technical efficiency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".