Comparative Jurisprudence on the Application of the Arm’s Length Principle: A Global Survey of Transfer Pricing Regulation and its Implications for Zambia
Bibliographic record
Abstract
The Arm’s Length Principle (ALP), anchored in Article 9 of the OECD Model Tax Convention, remains the cornerstone of global transfer pricing regulation. Yet, its practical application exhibits wide divergence across jurisdictions, shaped by institutional capacity, economic structure, legislative evolution, and political will. This article undertakes a comparative analysis of how selected countries - including the United States, United Kingdom, China, Brazil, Canada, Germany, India, Uzbekistan, Kenya, South Africa, and Zimbabwe - have implemented and enforced the ALP within their transfer pricing frameworks. Using doctrinal, functional, and policy-analytical approaches, the study critically assesses the congruence or departure from OECD standards, the robustness of local legislation, the sophistication of enforcement mechanisms, and emerging jurisprudence. It draws implications for Zambia, arguing that the country’s adoption of ALP-based transfer pricing regulation must be informed by nuanced understanding of international legal transplant theory, contextual enforcement limitations, and the need for policy coherence between domestic imperatives and international obligations. The study concludes by advocating for a hybrid, context-sensitive ALP application model tailored to Zambia’s institutional and developmental needs.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".