Bibliographic record
Abstract
Fulfilling its campaign promise, the new NDP government announced a review of Alberta’s royalty framework in June 2015. The province receives royalty revenue from three main sources – natural gas, crude oil, and oil sands. Since the 2009-10 fiscal year the largest contributor to Alberta’s royalty revenues has been the oil sands. If you want a sense of how important oil sands royalties have been for Alberta’s finances, consider this: In the 2014–15 fiscal year, the government collected just over $5 billion from oil sands royalties. These royalties covered over 10 per cent of the province’s operational expenses of $48.6 billion in the same fiscal year. Over the last six fiscal years the oil sands have contributed an average of 10 per cent of revenues to provincial coffers. This makes oil sands royalties the fourth largest contributor behind personal income taxes (23 per cent), federal transfers (13 per cent) and corporate income taxes (11 per cent). But how many Albertans really understand how the royalty system works? What do we mean when we say “royalty”? How does the Alberta Government calculate royalties on oil sands producers? If the system is going to change, it’s important that Albertans understand how the current system works. That is what this paper is designed to do. For Albertans to properly judge the impact of new policy, they need a solid understanding of the current policy environment. We all know that oil prices have dropped and oil sands producers are losing profitability. As such, changes to the royalty system could have a deep and profound impact on the sector. Here are some of the issues this primer will study: • Pre-payout projects vs. post-payout projects, in other words, the classification of projects for royalty purposes based on whether the cumulative costs of a project exceed its cumulative revenues • Monthly payment of royalties vs. annual payment • Understanding the unit price of bitumen and how that price is applied • Gross vs. net revenues and the application of royalties • How the price of oil and the exchange rate between Canadian and U.S. dollars impact royalties • The historical and forecast contribution of oil sands royalties to Alberta’s finances Needless to say, a primer like this should be required reading for policymakers. It should also be required reading, however, for any Albertan who cares about the long-term benefit of the oil sands to Alberta’s revenue, and our financial future as a province.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".