Differences in Interprovincial Productivity Levels
Bibliographic record
Abstract
This study examines provincial differences in productivity (GDP per job) using decomposition and regression analysis. In the first stage of the study, the relative size of productivity differences across provinces is examined. Then, these differences are decomposed into two components - the first is the portion of the difference that arises from industry-mix, and the second is due to real productivity differences at the industry level. The paper also examines the contributions of the new and old economy sectors to differences in provincial productivity. Finally, regression analysis is performed in order to determine the statistical significance of interprovincial productivity differences. The paper finds that British Columbia, Alberta, Saskatchewan, Ontario and Quebec do not differ significantly from another in terms of GDP per job after differences in industry mix are considered. Manitoba and the Atlantic Provinces lag behind the others. Most of the difference in the latter two cases stems from real differences at the industry level rather than from the effect of differences in industry mix. The Natural Resources sector plays an important role in bolstering the performance of Alberta and Saskatchewan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".