Une concurrence fiscale loyale (un compte de fée?) /
Bibliographic record
Abstract
Tax competition between tax sovereignties is a fact. We focus here on the international tax competition. Taxation is one of the tools of governance that States use to direct their policies. Tax authorities try to diminish the burden of their taxpayers to improve the national economic and social welfare. To aim this objective, Governments intensify the competitiveness of the domestic trade and/or attract foreign investments. Because every States share the same goal, Governments compete with each other on the tax field. This tax competition is qualified as beneficial on the one hand, and one the other hand---ever more often---the adjective used to qualify this competition would be "harmful". At first, this thesis exposes the situation of tax competition, presenting the opposing views and the concurring ones. Then, we look at the position of the Organization of Economic Cooperation and Development and the position of the European Union on this issue of tax competition. Historic summaries explain the point of view of these two organizations that are the leaders in the fight against the "harmful" tax competition. Finally, we give subjective ideas to re-think tax competition in a fair way.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.219 | 0.054 |
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".