The Taxation of Petroleum and Minerals
Bibliographic record
Abstract
Preface Dominique Strauss-Kahn 1. Introduction Philip Daniel, Michael Keen Charles McPherson (IMF) Part 1: Conceptual Overview 2. Theoretical perspectives on resource tax design Robin Boadway (Queens University, Canada) and Michael Keen (IMF) 3. Principles of resource taxation for low-income countries Paul Collier (University of Oxford) Part 2: Sectoral Experiences and Issues 4. Petroleum fiscal regimes: Evolution and challenges Carole Nakhle (University of Surrey, UK) 5. International mineral taxation: Experience and issues Lindsay Hogan (Australian Bureau of Agricultural and Resource Economics) and Brenton Goldsworthy (IMF) 6.' Natural gas: Experience and issues Graham Kellas (Wood Mackenzie) Part 3: Special Topics 7. Evaluating fiscal regimes for resource projects: An example from oil development Philip Daniel, Brenton Goldsworthy, Wojciech Maliszewski, Diego Mesa Puyo (all IMF) and Alistair Watson 8. Resource rent taxes: A re-appraisal Bryan Land (World Bank) 9. State participation in the natural resources sectors: Evolution, issues and outlook Charles McPherson (IMF) 10. How best to auction natural resources Peter Cramton (University of Maryland) Part 4: Implementation 11. Resource tax administration: The implications of alternative policy choices 12 Resource tax administration: Functions, procedures and institutions Jack Calder 13. International tax issues for the resources sector Peter Mullins (Australian Tax Office) Part 5: Stability and Credibility 14. Contractual assurances of fiscal stability Philip Daniel (IMF) and Emil Sunley 15. Time consistency in petroleum taxation: Lessons from Norway Petter Osmundsen (University of Stavanger, Norway)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.017 |
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".