Consultant Lobbyists and Public Officials: Selling Policy Expertise or Personal Connections in Canada?
Bibliographic record
Abstract
Recent research suggests that there are two different types of lobbyists: those specializing in providing access to their personal connections with public office holders, and those specializing in a particular policy sector. This article advances this research by examining the actual behaviour of consultant lobbyists with data gathered from the Canadian Lobbyist Registry. Specifically, we probe two questions. First, using four indicators found within the literature, we investigate whether the behaviour of consultant lobbyists reflects the well-connected generalist or the issue specialist lobbyist. Second, we examine moving public office holders to see whether administrative officials – who make greater use of technical information – or politicians and partisan advisors – who are more interested in partisan/political information – are more likely to continue to be contacted by consultant lobbyists who contacted them in their previous position. Our results suggest that a more nuanced understanding of lobbying is required. While the majority of activities by consultant lobbyists are consistent with providing expertise to policymakers, a sizable minority of lobbyist activity is consistent with selling access to public office holders. Yet even here, our second analysis suggests that personal relationships may also involve the provision of expertise.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".