Victims of their own success abroad? Why the withdrawal of US transparency rules is hindered by diffusion to the EU and Canada
Bibliographic record
Abstract
Recent years have seen significant efforts to reduce corruption in the oil, gas and mineral industries. Under the Obama administration, rules were adopted obliging stock-exchange-listed extraction companies to disclose payments to domestic and foreign governments, an initiative which soon spread to the European Union and Canada. Under Trump, however, policy preferences changed, and the disclosure requirements were withdrawn. This article investigates how diffusion of United States (US) disclosure rules has mitigated the effects of the withdrawal process through insights on norm diffusion, market power and rules applicable beyond states’ territorial borders. It is argued that when (1) rules with broad external applicability (2) diffuse to multiple influential jurisdictions and (3) address large multinationals in (4) an internationally interdependent sector, global standards of regulation may emerge. As these conditions are largely (although not entirely) fulfilled, it is likely that most large US multinationals will remain at least partially subject to payment disclosure obligations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".