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 Under Trump, however, policy preferences changed, and the disclosure requirements were withdrawn.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 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".