CENTRE FOR THE STUDY OF LIVING STANDARDS An Analysis of British Columbia’s Productivity, 1997-
Bibliographic record
Abstract
The report, based on the CSLS Provincial Productivity Database, provides an overview of British Columbia’s productivity performance over the 1997-2007 period. The key findings are the following: • British Columbia experienced weak labour productivity growth in the market sector from 1997 to 2007, with an average growth rate of only 1.2 per cent per year, significantly below the national average of 1.7 per cent per year. This was due to weak capital intensity growth compared to the national average (1.6 per cent vs. 2.3 per cent), as well as weak labour quality growth (0.1 per cent vs. 0.5 per cent). In terms of labour productivity growth, British Columbia’s performance ranks 9th among the provinces, only above Alberta. • Labour productivity growth in the province was driven mainly by capital intensity growth, which accounted for 52.2 per cent of the increase experienced over the 1997-2007 period. Multifactor productivity growth also played an important role, accounting for 40.6 per cent of labour productivity growth. Finally, a small but steady increase in labour quality was responsible for 6.5 per cent of the labour productivity growth experienced in the province. • Despite low labour productivity growth overall, the manufacturing and utilities industries in British Columbia had the highest growth rates compared to equivalent industries in the other
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".