THE STUDY OF LIVING STANDARDS An Analysis of Alberta’s Productivity, 1997-2007: Falling Productivity in Mining, and Oil and Gas Extraction Severely Dampens Market Sector Labour
Bibliographic record
Abstract
The report, based on the CSLS Provincial Productivity Database, provides an overview of Alberta’s productivity performance over the 1997-2007 period. The key findings are the following: • Alberta’s labour productivity grew at an average annual rate of 1.0 per cent during the 1997-2007 period, well below the national average of 1.7 per cent per year. In terms of labour productivity, Alberta’s performance ranked 10th among the provinces due to poor performance in its largest sector, mining, and oil and gas extraction. However, Alberta ranked 1st using the equally weighted rankings due to strong growth in most industries. • The following two industries in Alberta enjoyed the highest labour productivity growth rates in Canada when compared to equivalent industries in the other provinces: retail trade (4.9 per cent per year), and information and cultural industries (5.3 per cent). • Labour productivity growth in both Alberta and Canada was driven mainly by increases in capital intensity. However, capital intensity growth played a much larger role in Alberta, where it amounted to over 100 per cent of growth as multifactor productivity experienced a decline. Indeed capital intensity growth was the fastest among the ten provinces. • Alberta’s labour productivity level was $39.4 (1997 dollars) per hour in 1997, which represents
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".