Bibliographic record
Abstract
This paper seeks to explore the above question by drawing insights from existing literature. The existing body of literature is rich in documenting the phenomenon of legal transplants, theorising why legal transplants take place and presenting the debate on the relevance of local culture to the adaptation of the transplants. In terms of tax transplants, there is an emerging strand of literature that examines the unique characteristics of tax transplants, the common core of tax law, and the global tax convergence. However, there is a lack of analysis focusing on the critical role of processes in legal and tax transplants. Through a case study of the Chinese tax transplants, this paper seeks to test the theories of legal transplants and to demonstrate the importance of process in successful transplants. It examines two types of processes: tax processes and transplantation processes. Tax processes include the political process, administrative and compliance process, and dispute resolution process. Tax transplantation processes include the process of selection, translation, adoption and adaptation. The central claim of this paper is that processes matter in tax transplants. Taxation science is capable of being borrowed and duplicated. Tax processes in a country are defined by its general political, legal and institutional culture, and thus vary from country to country. Many tax rules or principles are the outcomes of tax processes in the country of origin and their implementation depends on a certain set of administrative processes. The transplantation of a tax rule or principle is likely to fail if it is done without the necessary processes, or at least, without a full appreciation and accommodation of the differences in processes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".