International Mergers and Acquisitions : A Country by Country Tax Guide
Bibliographic record
Abstract
Taxation of Mergers and Acquisitions in Argentina (D.E. Rybnki, et al.). Taxation of Mergers and Acquisitions in Australia (T. Carberry and G. Leyden). Taxation of Mergers and Acquisitions in Azerbaijan (A. Bati). Taxation of Mergers and Acquisitions in Canada (G.C. Boehmer and M. Vantil). Taxation of Mergers and Acquisitions in Finland (K. Hiltunen and J. Sivonen). Taxation of Mergers and Acquisitions in France (J. Girard, et al.). Taxation of Mergers and Acquisitions in Germany (D. Endres and S. Ditsch). Taxation of Mergers and Acquisitions in Ireland (D.P. Clarke and D.J. Rorke). Taxation of Mergers and Acquisitions in Italy (R. Lazzarone and F.C. Papa). Taxation of Mergers and Acquisitions in Japan (K. Hayashi and A. Zencak). Taxation of Mergers and Acquisitions in Kazakhstan (A. Kenjebayeva, et al.). Taxation of Mergers and Acquisitions in Korea (K. K. Cook). Taxation of Mergers and Acquisitions in Mexico(J.Gonzales-Bendiksen). Taxation of Mergers and Acquisitions in the NEtherlands (O.E. van der Donk, et al.). Taxation of Mergers and Acquisitions in Norway (E. Ommedal). Taxation of Mergers and Acquisitions in Russia (J.M. McDonald, et al.). Taxation of Mergers and Acquisitions in Spain (R. Reyero, et al.). Taxation of Mergers and Acquisitions in Ukraine (O.V. Batyuk and V.N. Zakhvataev). Taxation of Mergers and Acquisitions in the United Kingdom (N.N. Davison and J. Hillian). Taxation of Mergers and Acquisitions in the United States (M.C. Claybon, et al.).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.102 | 0.096 |
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".