Diversity and Employment Growth in Canada, 1971-2001: Speciality, Diversity and Restructuring
Bibliographic record
Abstract
In this paper, we explore the link between diversity in the local economy and \nsubsequent employment growth. To do so, we first examine diversification trends \nbetween 1971 and 2001 across 382 Canadian regions (urban and rural). We then \nexamine whether or not the more diversified regions display faster employment growth. \nAlthough they do—which is evidence of the effect of urbanization economies—an \nanalysis of changes in economic structure suggests that the link between the process of \ndiversification and employment growth is complex. Because specialization can also lead \nto employment growth, and because the link between the process of diversification and \nemployment growth is not systematic, we suggest that diversification policies will be \ndifficult to implement successfully. We also emphasize the importance of distinguishing \nbetween diversity and speciality. Diversity is measured at the level of a regional \neconomy. Speciality is sometimes interpretedas being sector-specific (and as such, is \nnot directly related to diversity), but is sometimes interpreted as characterizing a \nregional economy (and as such, is the opposite of diversity).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".