When Data Comes Home: Next Steps in International Taxation’s Information Revolution
Bibliographic record
Abstract
Over the last decade, there has been a revolution in cross-border tax information exchange and reporting. While this dramatic shift was the product of multiple forces and events, a fundamental reality is that politics, technology, and law intersected to drive the shift to the point where nation-states will now transmit and receive from each other significant ongoing flows of taxpayer information. States can now expect to accumulate large stashes of data on cross-border income, assets, and activities on a scale and level of comprehensiveness unmatched by previous information exchange regimes. This article examines the pressing follow-up question of how this data will be used and what issues nation-states will confront when data comes home. Although concerns about data protection and use have been raised in critiquing the new cross-border information exchange regimes, a systematic examination of how governments might use or fail to use data and when those uses will pose unacceptable risks has yet to be undertaken. This article analyzes how domestic politics, priorities, and institutions are likely to affect tax enforcement and data usage at the nation-state level going forward. We argue that despite the dominant focus on global developments, domestic politics and technological constraints will likely play an equally if not more significant role in data use and protection as countries receive data and decide what to do with it. The mere fact that collective political will on a global level produced the information revolution does not prevent domestic forces from either derailing the revolution in practice or redirecting data to other uses. This article maps the potential risks and examines the extent to which individual nation-states will have the capacity or inclination to conduct enforcement, protect taxpayer privacy, and attend to distributional outcomes and risks. We ultimately articulate a framework for understanding the country-level factors likely to affect outcomes and pathways when data comes home.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".