The Long and Short of the Canadian-US Free Trade Agreement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it