The political origins of corporate transparency: forging strange coalitions through information rules and policy entrepreneurship
Bibliographic record
Abstract
When does business support corporate transparency laws, and how do they succeed despite opposition from other powerful business groups? Existing research converges on a common causal pathway: Crises increase public issue salience and open windows of opportunity for policy entrepreneurs to pressure politicians into adopting transparency laws. Examining the case of beneficial ownership transparency (BOT) laws, I theorize an alternative causal pathway where past policy decisions mandating information collection by certain industries produce inter-industry divergence in their policy preferences and undermine opposition business lobbying. Civil society groups can then engage in policy entrepreneurship to integrate supportive regulated industries into new coalitions. Large, organizationally diverse ‘strange coalitions’ increase political pressure on policymakers, leading to the adoption of corporate transparency laws. I conduct a structured, focused comparison of the United States, United Kingdom, Canada, and Australia as parallel demonstrations of this causal pathway. I combine primary and secondary source documentation with 44 semi-structured interviews to trace failed and successful attempts to adopt BOT laws during the 2010s and early 2020s. I center endogenous feedback processes as a primary cause of transparency, provide further evidence of when firms prefer stronger regulation, and highlight the continued importance of domestic interests in transnational policy issues.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".