Multilateral advance pricing agreements - A multifaceted approach to a global conundrum?
Bibliographic record
Abstract
There is currently a revival of interest in multilateral advance pricing agreements (MAPAs), not only at the Organisation for Economic Cooperation and Development (OECD) level, but also among revenue authorities. The revenue authorities in the United States, Canada and the United Kingdom have recently demonstrated an interest in multilateral approaches to transfer pricing controversy resolution. This article examines this revived interest in MAPAs before going on to consider some past multilateral agreements, in order to gain valuable insights into the multilateral process going forward. The views of some of the main protagonists in these agreements are discussed, including reflections from the coordinator of the APA Programme in the UK until 2012, a competent authority negotiator in many of the world's past MAPAs. Current economic, fiscal and accounting developments have made transfer pricing a top priority concern for all multinational enterprises (MNEs), there has been an unprecedented rise in international collaboration between tax administrations, and MAPAs are the only transfer pricing controversy management tool to simultaneously offer multiple revenue authorities and MNEs prospective certainty. This article contributes to the study of tax law and policy in that it examines the advantages and disadvantages of prospective multiparty dispute resolution in this globally important area.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".