Bibliographic record
Abstract
This document contains four installments of Big Picture, a column published in Tax Analysts, International Tax Notes, Volumes 66-68 (2012). The first installment, Putting Arbitration on the MAP: Thoughts on the New U.N. Model Tax Convention, discusses the addition of mandatory arbitration to the UN Model Tax Treaty and argues that what is contemplated is not really arbitration as such but more in the nature of expert determination, and that the provisions exact high costs, especially on developing countries (which should be wary about signing tax treaties with rich countries in any event). The second, Do We Need to Know More About Our Public Companies? discusses the problem of public opacity in the global tax dealings of multinationals, introduces readers to the corporate tax transparency provisions enacted in the Dodd-Frank Wall Street Reform Act of 2010, and argues that publicity of high-profile tax dodging and offshore cash-hoarding makes the case for greater public accountability by multinationals. The third, Could a Same-Country Exception Help Focus FATCA and FBAR? discusses the recent US crackdown on offshore tax evasion, and introduces readers to the likely inadvertent application of this regime to millions of dual-citizens who live and work overseas and thus have bank accounts overseas as well. It proposes a move toward the global standard of residence-based taxation, incrementally if necessary, to ensure the FATCA and FBAR target hits the intended mark. The fourth and final instalment of 2012, Measuring a Fair Share, discusses what politicians mean and what they overlook when they say that taxpayers ought to pay a fair share; it argues that in failing to adequately define who ought to be considered a taxpayer and what ought to be considered their available resources, governments have essentially constructed an arbitrary and ultimately unjust parameter around the discussion of what fairness means in taxation.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.331 | 0.201 |
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".