Sharing Tax Information in the 21st Century: Big Data Flows and Taxpayers as Data Subjects
Bibliographic record
Abstract
In the last 10 years, governments have initiated several reforms to automatically exchange bulk taxpayer information with other governments (mainly via the Foreign Account Tax Compliance Act, the common reporting standard, and country-by-country reporting). This enhanced sharing of tax information has been encouraged both by technological change, including digitization, big data, and data analytics; and by political trends, including governments' efforts to reduce offshore tax evasion and aggressive international tax avoidance. In some cases, however, legal protections for taxpayer privacy and other interests are insufficiently robust for this emerging international sharing framework. Conceptually, taxpayers should be seen as "data subjects" whose rights are proactively protected by data protection laws and policies, including fair information practices. An optimal regime, which would balance the interests of taxpayers against those of tax authorities, should include a multilateral taxpayer bill of rights, a cross-border withholding tax that could be imposed in lieu of information exchange, and a global financial registry that would allow governments to identify the beneficial owners of business and legal entities.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".