The Treaty Network Theory: Accessing Foreign Tax Information Networks Under the OECD Model Convention
Bibliographic record
Abstract
Globalization and the current economic climate have forced states to work toward improving access to foreign and domestic tax information with a view to better protecting their own tax base. At the forefront of these efforts are the exchange-of-information provisions found in virtually all bilateral tax treaties, many of which are based on the model double taxation convention of the Organisation for Economic Co-operation and Development (OECD). Curiously, existing departures from the OECD model suggest that certain states may have the obligation to assist one treaty partner by requesting and providing tax-related information from another treaty partner. This article analyzes the viability of this "treaty network theory" in the context of the OECD model and Canada's existing tax treaties. The author concludes that the text of many of Canada's treaties appears to allow for such a result, suggesting that tax authorities and treaty negotiators should carefully consider, in their future work, whether this was intended, or whether they should protect against this possibility.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".