MNEs and the International Cooperation on Tax Administration: The Evolution of Attitude and Status
Bibliographic record
Abstract
Multinational Enterprises have become a main driver in the creation of international tax administration, and the relationship between MNEs and the international cooperation on tax administration can be viewed in two dimensions. on the one hand, MNEs are the most important regulation object of the international cooperation on tax administration. On the other hand, MNEs are a driving force of the creation, challenges, changes and developments of the international tax cooperation on tax administration. As a result, the relationship between MNEs and the international tax cooperation on tax administration should be realized in a two-fold transitions: the first one is the attitude aspect, which requires the transition from confrontation and passive obedience stage to voluntary compliance stage. While the second aspect lies in MNEs' status, which requires the transition from the regulation object to aparticipant of the international tax cooperation on tax administration. This subject has attracted the international community's attention, and a lot of further investigations and legal practices have been conducted recently. China should also make a passive response through such measures as to strength the dialogue and cooperation with MNE taxpayers, to provide tailor-made taxation services, and to build risk feedback mechanism for large taxpayers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".