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Record W3174176296 · doi:10.55016/ojs/sppp.v4i1.42364

Plucking the Golden Goose: Higher Royalty Rates on the Oil Sands Generate Significant Increases in Government Revenue

2011· article· en· W3174176296 on OpenAlexafffundabout
Kenneth J. McKenzie

Bibliographic record

VenueThe School of Public Policy Publications · 2011
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversity of Calgary
FundersUniversity of TorontoQueen's UniversityAustralian GovernmentUniversity of Calgary
KeywordsPluckingRevenueGooseOil sandsBusinessAgricultural economicsEconomicsArchaeologyBiologyGeographyFinanceEcology

Abstract

fetched live from OpenAlex

The Alberta government’s 2009 New Royalty Framework elicited resistance on the part of the energy industry, leading to subsequent reductions in the royalties imposed on natural gas and conventional oil. However, the oil sands sector, subject to different terms, quickly accepted the new arrangement with little complaint, recognizing it as win-win situation for industry and the government. Under the framework, Alberta recoups much more money in royalties — about $1 billion over the two year period of 2009 and 2010 — without impinging significantly on investment in the oil sands. This brief paper demonstrates that by spreading the financial risks and benefits to everyone involved, the new framework proves it’s possible to generate increased revenue without frightening off future investment. The same model could conceivably be applied to the conventional oil and natural gas sectors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.480

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.056
GPT teacher head0.266
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes3
Has abstractyes

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