The Canadian Manufacturing Sector, 2002-2008: Why Is It Called Dutch Disease?
Bibliographic record
Abstract
High-profile complaints about so-called Dutch disease have led many to question if industry in some parts of the country is suffering due to the success of the natural resources sector in others. This paper considers changes in manufacturing employment from 2002- 2008, a time of increased commodity prices. At first glance, the figures appear alarming — Canada shed 328,000 manufacturing jobs during that period — but the decline wasn’t entirely commodity-driven. Canada is the sole G-7 country in which manufacturing is on par with what it was 40 years ago; manufacturing employment rose in the decade prior to the decline thanks to government austerity, which spurred monetary easing, making industry more export-competitive. Much of the contraction from 2002-2008 was a natural reaction to this unsustainable situation. Higher commodity prices in the same period actually had a benign — if not positive — effect on Canada’s manufacturing industry, notwithstanding the fall in employment. The manufacturing jobs that were lost were typically low paying, and were offset by the creation of betterpaying employment in other sectors. The available data on gross employment flows suggest that the disruptions associated with the shift of employment out of manufacturing were surprisingly small. The reduction in employment was largely achieved through attrition; layoff rates held steady while hiring rates fell. Moreover, the data are not consistent with fears that the manufacturing sector was hollowed out. Research and development activities held steady and investment in new technology continued to grow, leaving the manufacturing sector healthier in 2008 than it was in 2002.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".