Size, Role and Performance in the Oil and Gas Sector
Bibliographic record
Abstract
The oil and gas sector is a key driver of the Canadian and Albertan economies. Directly and indirectly it typically accounts for roughly half of Alberta’s GDP, as well as one-third of the country’s business investment and a quarter of business profits — and rising global demand will only add to these figures. However, that energy sector is also a changeable place populated by companies of all shapes and sizes, from small Emerging Juniors to wellestablished Majors whose daily production capacities are hundreds or thousands of times greater. The sector’s assorted firms have different structures and ambitions, respond in distinct ways to market forces and have unique impacts on the economy. These differences in size, role and performance must be reflected in energy and related economic policies if they are to be effective in achieving policy goals. For example, they must recognize that the smallest firms are not always the fastest growers or the most innovative; that Intermediates are the most highly leveraged, with the highest debt-to-equity ratios; and that while Majors tend to have the lowest average cost per well drilled, they also (along with Emerging Juniors) have the highest operating costs. Despite the industry’s critical importance, relatively little hard data has been made available concerning companies’ structure, behaviour and performance, based on size. This paper goes a considerable way toward filling that gap, bringing together comprehensive datasets on 340 public oil and gas firms to chart essential patterns and trends, so policymakers and industry watchers can better understand the complexity and functioning of this important sector.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".