Rethinking royalty Rates: Why There Is a Better Way to Tax Oil and Gas Development
Bibliographic record
Abstract
When provinces raise royalties charged on oil and gas production, the result can be less, not more tax revenues. The authors show how resource-rich provinces would be better off relying more on auctions for exploration and development rights and relying less on royalties levied on output. Oil and gas taxation in Canada consists of two main elements: an auction payment and royalties that apply to the value of resources extracted. The authors examine the results of Alberta’s short-lived decision, in 2007, to increase royalty rates on oil and gas production. Accounting for differences in bonus bids across provinces in the same geological zones, the authors report that Alberta government revenue, collected through bonus bids, declined by nearly as much as the projected increase in royalty payments.
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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 | 0.017 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.047 | 0.043 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".