Putting the Reign Back in Sovereign: Advice for the Second Obama Administration
Bibliographic record
Abstract
In its first term, the Obama administration enacted two pieces of legislation, each designed to protect an increasingly vulnerable income tax base, and each of which had the potential to set a new and unprecedented course for no less than the regulation of the global economy by the nation-state. The first, the Foreign Account Tax Compliance Act (FATCA), sought to end global tax evasion through tax havens. The second, a little-noticed two-page addendum to the Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank), sought to end the contribution of American multinationals to corruption in governance by codifying the transparency principles of the global Extractive Industries Transparency Initiative (EITI). Both of these reforms reasserted a role for the nation state in regulating people and resources. But neither has yet to fulfill its potential. First, each has raised difficult questions about what the state can and cannot do to enforce disclosure and compliance on a global basis; failing to answer these questions is impeding implementation and aggravating an already-flagging taxpayer morale. Second, neither is broad enough: FATCA should be truly reciprocal and EITI should expand beyond the extractive industries. By acknowledging and responding in a principled way to the obstacles that limit their effectiveness, a second Obama administration could take significant steps to bring each piece of legislation to its potential, while ensuring that its scope focuses on its intended target in each case. This article outlines how these proposals could be accomplished and makes the case that they should be attempted.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.034 | 0.052 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".