Regulating Lobbyists: A Comparative Analysis of the United States, Canada, Germany and the European Union
Bibliographic record
Abstract
Lobbying is central to the democratic process. Yet, only four political systems have lobbying regulations: the United States, Canada, Germany and the EU (most particularly, the European Parliament). Despite the many works offering individual country analysis of lobbying legislation, a twofold void exists in the literature. Firstly, no study has offered a comparative analysis classifying the laws in these four political systems, which would improve understanding of the different regulatory environments. Secondly, few studies have analysed the views of key agents—politicians, lobbyists and regulators—and how these compare and contrast across regulatory environments. We firstly utilise an index measuring how strong the regulations are in each of the systems, and develop a classification scheme for the different ‘ideal’ types of regulatory environment. Secondly, we measure the opinions of political actors, interest groups and regulators in all four systems (through questionnaires and elite interviews) and see what correlations, if any, exist between the different ideal types of system and their opinions. The conclusion highlights our findings, and the lessons that can be used by policy‐makers in systems without lobbying legislation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".