What You Give and What You Get: Reciprocity Under a Model 1 Intergovernmental Agreement on FATCA
Bibliographic record
Abstract
As is well known within international tax circles by now, the U.S. Congress enacted FATCA in response to publicity surrounding well known foreign institutions, most especially in Switzerland, that helped US customers hide income and assets from the IRS. That publicity continues, reinforcing the need for the protection of the US tax base against erosion through criminal activity. Thus FATCA emerges as a defensive move against criminal behavior. But in the absence of reciprocity from the US itself, the reverse proposition remains possible: the United States perversely positions itself to gain from the very behavior it seeks to eliminate in other jurisdictions. This brief look at what countries give and what they get under an IGA with the US signals the vital role of reciprocity in making sure countries use international agreements to gain mutual advantage through cooperation rather than a unilateral edge in a dangerous game of undermine-thy-neighbor.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".