An Assessment of the Efficiency of Canadian Power Generation Companies with Bootstrap DEA
Bibliographic record
Abstract
Power generation companies play an important role in the Canadian economy, as most of the economic activities in the manufacturing and service sectors are powered by electricity. The significance of the Canadian power generation industry shows that efficiency analysis is essential for efficiently managing power generation and distribution in Canada. However, there have been few attempts to study the relative efficiencies of the Canadian power generation companies. This study fills in this gap by assessing the overall technical, managerial, and scale efficiencies of a sample of Canadian power generation companies via the non-parametric bootstrap DEA methodology, with firm-level annual inputs and outputs data over an 18-year horizon. The results of our investigation indicate low levels of overall technical and managerial efficiencies but relatively high levels of scale efficiencies of the Canadian power generation companies over the entire study period. We also found that the 2007–2009 financial crisis impacted the relative performance of the Canadian power generation companies. Our results also allowed us to identify the benchmark power generation companies for each type of efficiency that the inefficient companies should target toward improving their efficiency.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".