“Americans: We Love You, But We Can’t Afford You”: How the Costly U.S.-Canada FATCA Agreement Permits Discrimination of Americans in Violation of International Law
Bibliographic record
Abstract
On February 20, 2014, National Public Radio ("NPR") reported a record high number of American citizens renouncing their citizenship worldwide.1 In 2012, 932 individuals renounced their 1.See Ari Shapiro, Why More Americans are Renouncing U.S. Citizenship, 2015] "AMERICANS: WE LOVE YOU, BUT WE CAN'T AFFORD YOU" 613 U.S. citizenship or terminated their U.S. residency (termed "expatriating"). 2 In 2013 this number surged to 2,999, the highest number in history, and almost thirteen times the number of expatriates only five years earlier.3 Even more are expected to renounce their citizenship in 2014 and 2015 due to the newly implemented law. 4 The NPR article reported, "[w]hile individual reasons for renouncing may vary from person to person, experts in the field say the recent dramatic spike has more to do with the 2010 tax law [the Foreign Account Tax Compliance Act] than any other factor."5 In 2010, Congress passed the Foreign Account Tax Compliance Act ("FATCA"), 6 which requires all foreign financial institutions ("FFIs") doing business with the United States to collect information about their U.S.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 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".