Bibliographic record
Abstract
Abstract Who should tax multinationals? National political figures sometimes signal their assumptions by making superior or even exclusive claims about who may tax “their” multinational companies, and it is common to hear such companies or their incomes referred to as “belonging” to one nation or another. The rhetoric reflects conventional wisdom about sovereign nations and their assumed entitlements, and is often invoked to curb or even sanction the seemingly excessive tax jurisdictions of some nations. But this conventional wisdom often ignores the fundamental dependence of multinationals on ongoing, extensive, and multifaceted regulatory cooperation involving most of the nations of the world. The goal of this essay is to demonstrate that given this dependence, there are no clear legal or normative boundaries to virtually any asserted tax jurisdiction. The claim provides a solution for neither double taxation nor the problems associated with excessive tax competition, but the essay concludes that recognizing the dependence of governments and “their” multinationals on multilateral cooperation should lead to an increase in focus on how nations go about negotiating the terms of their cooperation on tax.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".