A Detailed Analysis of Newfoundland and Labrador's Productivity Performance, 1997-2018
Bibliographic record
Abstract
The main goal of this report is to describe and explain the trends in productivity in Newfoundland and Labrador, as well as trends in the variables used in the calculation of productivity, including output, labour input, and capital input. The main take-away from the report is the importance of the oil and gas sector to the economy of Newfoundland and Labrador. That sector has been responsible for most of Newfoundland and Labrador's economic growth, and now accounts for the largest share of the province’s business sector value added among 2-digit NAICS subsectors, even though it employed only 3.8 per cent of the province’s business sector workers in 2018. Due to the size of the mining and oil and gas extraction sector, its productivity performance strongly affects the performance of the overall business sector, which continues to represent a major challenge for the province. However, looking at the business sector excluding mining and oil and gas, productivity growth does fare better. The data can be split in two periods. Driven by the mining and oil and gas extraction sector, Newfoundland and Labrador’s overall productivity experienced impressive growth from 1997 to 2007, with real business sector productivity advancing at a compound annual rate of 6.0 per cent. The situation changed dramatically after 2007 when oil and gas productivity plummeted. Real business sector productivity in the province declined during the 2007-2018 period at a rate of 1.2 per cent per year
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".