The concept, practice and culture of lobbying in the English speaking countries
Bibliographic record
Abstract
The regulation of lobbying is a current topic both at the level of international organizations as well as in many European and overseas countries. This work deals with the comparison of the rules on lobbying in selected English-speaking countries. Descriptive, doctrinal and comparative methods are used to analyze the main trends in the lobbying regulation of the United States, Canada, the United Kingdom of Great Britain and Northern Ireland as well as Australia. The text is structured into four chapters. The first chapter deals with the definition of lobbying and its differences from corrupt dealings; it also covers the types of lobbying activities and the various kinds of lobbyists. The second chapter attempts to contextualize lobbying into the theories of the decision-making process. A comparison of the similarities and differences of the lobbying rules is made and analyzed in the third and fourth chapters. Both the third and the fourth chapter have a similar structure -- first, the general rules and approaches to regulation are introduced, and then a comparison of selected English-speaking countries is made. The third chapter deals with the most common rules for lobbyists; the fourth chapter focuses on the relatively neglected side of lobbying contacts, i.e. the rules for the targets of lobbying (public office holders). In the end, some measures and recommendations for the Czech Republic are also outlined.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".