Lobbying in the Shadows: A Comparative Analysis of Government Lobbyist Registers
Bibliographic record
Abstract
Policy Points Our research provides a starting point to benchmark government transparency measures to improve the quality and accessibility of information about lobbying. Policymakers and regulators can use our framework to develop or strengthen their own lobbying registers. Countries like Canada are a good example of how to design an accessible database about lobbying, and Chile, Ireland, and Scotland provide examples of comprehensive contact logs. CONTEXT: Information about lobbying is crucial to alert the public about undue influence in government decision making. Yet, government disclosures of lobbying activities are rare internationally and vary considerably in their completeness and accessibility. Building on a framework to measure lobbying transparency, this study benchmarked national government disclosures to understand what information was shared and to develop recommendations to strengthen political transparency. METHODS: To identify lobbyist registers, we reviewed four international surveys that assessed lobbying transparency in 128 countries. For each country with an online register, we assessed the 50 indicators in the Framework for Comprehensive and Accessible Lobbying (FOCAL), generating an overall score for each government. To highlight the importance of transparency for public health, we compared the visibility of tobacco industry lobbying across all registers by documenting the information provided about two prominent tobacco companies: Philip Morris International and British American Tobacco. FINDINGS: We identified 28 countries with online lobbyist registers, all from upper or upper-middle income countries. No country fulfilled all 50 indicators in the FOCAL. The category of "scope" had the highest scores across countries, whereas the "revolving door" and "financial" categories had the lowest scores. We found evidence of lobbying by Philip Morris International, British American Tobacco, or one of their subsidiaries in 14 of the 28 countries with online lobbyist registers. CONCLUSIONS: Our study empirically demonstrates the hidden nature of lobbying internationally. In the case of industries whose interests conflict with public health, poor lobbying transparency presents a risk that vested interests may undermine public health policymaking without anyone knowing. To ensure that health harming industry interests cannot escape scrutiny, public health advocates should support efforts to strengthen political transparency measures. Open access publishing facilitated by The University of Melbourne, as part of the Wiley - The University of Melbourne agreement via the Council of Australian University Librarians.
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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 | 0.002 |
| 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".