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Record W1615042186

What You Give and What You Get: Reciprocity Under a Model 1 Intergovernmental Agreement on FATCA

2013· article· en· W1615042186 on OpenAlex
Allison Christians

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsPublicityReciprocity (cultural anthropology)Law and economicsPolitical sciencePropositionEconomicsInternational economicsPolitical economyPublic economicsBusinessInternational tradeLawSocial psychologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0030.008
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.226
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it