Levels, but Average Productivity Growth
Bibliographic record
Abstract
The report, based on the CSLS Provincial Productivity Database, provides an overview of Ontario’s productivity performance over the 1997-2007 period. The key findings are the following: • Ontario’s labour productivity growth in the market sector was the same as the national average during the 1997-2007 period, 1.7 per cent per year. This is not surprising given the size of Ontario’s economy relative to Canada’s. More specifically, Ontario accounted for 37.8 per cent of Canada’s nominal GDP, and 40.0 per cent of total hours worked in Canada in 2007. Ontario’s performance ranked 7th among the provinces in terms of labour productivity growth. • In contrast to Canada, where labour productivity growth was driven mainly by increases in capital intensity, in Ontario the main driver was multifactor productivity growth, which was responsible for 48.1 per cent of total growth. Capital intensity growth accounted for 32.3 per cent of labour productivity growth, while labour quality accounted for 18.8 per cent. • Ontario’s labour productivity level in 2007 was $37.32 (1997 dollars) per hour, which represents 103.5 per cent of the Canadian level. This, in turn, implies a positive labour productivity differential of 3.5 percentage points. This positive differential was caused by a high multifactor productivity level, which was able to offset the low capital intensity level in Ontario’s market
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".