Bibliographic record
Abstract
Abstract Using newly constructed data for 88 Canadian industries (including primary, manufacturing and services), for 15 years (1992–2007), we analyse the impact of trade and technological change on labour demand, skill structure, wage premiums and welfare in Canada. Results show that export growth has no impact, whereas import growth reduces employment growth. But contrary to popular belief, Canada's job loss due to imports has been very small, only about 6,000 persons annually. China's negative impacts are more pronounced in industries where the share of information and communication technology (ICT) capital is rising fast and among low R&D intensive industries. In terms of skill change, ICT use and real exchange rate appreciation are biased towards high skill workers. Imports from the United States and China are skill‐neutral, whereas imports from Mexico are skill‐upgrading. Overall, neither export nor import growth has an impact on the wage rate. However, had there been no imports from China, the annual wage growth rate of high skill manufacturing workers would have been 0.6 per cent higher. Between 1992 and 2007, there was an annual net gain from the rise in imports at about 0.4 per cent of GDP, in addition to the gains obtained from 1992 import levels vis‐à‐vis autarky.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".