Les modalités et les caractéristqiues du processus d’adaptation de l’industriel canadienne à la concurrence internationale
Bibliographic record
Abstract
The developing countries' inroads into the traditional strongholds of the developed countries are seen by some industrialized countries as a threat. In any event, the situation is forcing them to make structural adjustments, with the inherent costs this imposes on business and labour. The primary goal of this article is to determine whether these changes threaten to « deindustrialize » Canada. Using an analysis of Canadian industry's trade performance in 150 categories and sub-categories of manufactured goods, the author concludes that Canadian business is indeed adapting to greater international competition and that the threat of deindustrialization has not yet materialized. The article's second objective is to discover the process by which this adjustment occurs. Concerning the business sector, the author found that businesses have adjusted to these changes primarily through a decline in the number of new firms or new plants, rather than through an increase in the number of closures. For labour, the results of a specially developed survey tend to indicate that the adjustment process also is not quite as harsh as might normally be expected. The author therefore sees little likelihood of major upheavals or massive worker displacement. He cautions, however, that we are still faced with transitional problems whose potential impact should not be underestimated.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".