Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it