Bibliographic record
Abstract
SECTION I. INTRODUCTION 1. Introduction Steven Elliott, Birke Hacker and Charles Mitchell SECTION II. ENGLISH LAW 2. Overpaid Taxes: A Hybrid Public and Private Approach Rebecca Williams 3. Mistaken Payments of Tax Duncan Sheehan 4. Restitution from Public Authorities: Any Room for Duress? Nelson Enonchong 5. Reasons for Restitution Charlie Webb 6. Restitutionary Claims by Indirect Taxpayers Charles Mitchell 7. Property, Proportionality, and the Change of Position Defence Niamh Cleary 8. Undoing Transactions for Tax Purposes: The Hastings-Bass Principle Monica Bhandari SECTION III. EUROPEAN LAW 9. Judicial Techniques in Relation to Remedies for Overpaid Tax Catherine Barnard and Julian Ghosh QC 10. The Principle of Effectiveness and Restitution of Overpaid Tax Maximilian Schlote SECTION IV. COMPARATIVE LAW 11. Absence of Basis: A German Perspective Anne Sanders 12. 'Public Law Restitutionary Claims': The German Perspective Birke Hacker 13. Overpaid Taxes and Constitutional Redress in Ireland Niamh Connolly 14. Restitution of Overpaid Tax in Canada Robert Chambers 15. Restitution of Unlawfully Exacted Tax in Australia: The Woolwich Principle Simone Degeling
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".