Citizens and the public perception of lobbying: do regulation and trust in political institutions make a difference?
Bibliographic record
Abstract
In political studies, lobbying is portrayed as a vital process of political participation, contributing information, policy capacities, and political capital to policymaking, but also as a potential source of representation biases, undue influence, and policy capture. Given such Janus-faced nature of lobbying within democracy, the primary aim of this article is to investigate which perception prevails among citizens empirically. By analysing the primary data of two surveys of 4000 Canadian and 1600 Swiss citizens, it investigates the public perception of lobbying across countries with contrasting institutional and regulatory frameworks and different levels of trust in political institutions. Results show that citizens' perception of lobbying differs in the two contexts, being predominantly negative in Switzerland and positive in Canada. Additionally, Swiss citizens are significantly more likely than Canadians to view lobbying as inadequately regulated. Both trust in political institutions and the perception that lobbying is properly regulated have a significant and positive impact on the perception of lobbying. Interestingly, despite Switzerland's higher levels of trust in political institutions than Canada, this trust does not translate into a more positive perception of lobbying, suggesting that robust regulations may play a more decisive role than institutional trust in shaping public perceptions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".