Technology and the demand for skills in Canada: an industry‐level analysis
Bibliographic record
Abstract
In this paper we examine the effect of technological change on the relative demand for skilled workers across Canadian industries. We find that skill upgrading at the aggregate level is less evident in Canada than in the United States and other industrialized economies over the 1981–94 period. Behind this overall trend on skill upgrading, there is substantial variation across industrial sectors. Consistent with the skill‐biased technological change hypothesis, the technology indicators – the stock of patents used by the industry and the age of capital stock – are found to be significantly correlated with skill intensity. JEL Classification: E24, J23, J31, O33 Technologie et demande de travailleurs qualifiés: une analyse au niveau de l'industrie. Ce mémoire examine les effets du changement technologique sur la demande de travailleurs qualifiés dans les industries canadiennes. On montre que l'amélioration du niveau des compétences au niveau agrégé est moins claire au Canada qu'aux Etats‐Unis et dans les autres économies industrialisées au cours de la période 1981–94. Derrière cette tendance générale, il y a cependant des variations importantes entre les secteurs industriels. On trouve que les indicateurs de niveau technologique (le stock de brevets, l'âge du stock de capital) sont co‐reliés positivement au degré d'intensité d'utilisation des compétences – ce qui s'arrime bien avec l'hypothèse du changement technologique liéà une amélioration du niveau des compétences
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".