Bibliographic record
Abstract
This article argues that the principle of economic allegiance, long regarded as the normative foundation of international tax jurisdiction, has become insufficient in the face of recent global reforms that expand the concept of nexus and fragment taxing authority. Nexus once served as a constraint, delimiting the states entitled to tax. Yet, as recent reforms broaden the scope of both source and residence, nexus increasingly admits multiple overlapping claims, transforming tax jurisdiction into a form of shared authority. The challenge today is no longer merely whether a state has jurisdiction but how to allocate jurisdictional fragments among states. The economic allegiance principle excludes states with no claim but offers no guidance for allocation. This article contends that fairness – in particular, inter-nation equity – must be reaffirmed as the central normative principle in this allocation step. It demonstrates how recent reforms fail to articulate coherent distributional standards and instead rely on arbitrary formulas and ordering rules that disproportionately benefit wealthier states. To avoid reproducing inequities, political compromises in international tax must begin from explicit distributional considerations before turning to technical design.
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.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".