Mobilization and Strategies: Comparing Trade Lobbying in the US and Canada
Bibliographic record
Abstract
Do US lobbying patterns extend to other countries? To date no study has systematically compared US lobbying patterns with those of other countries using observational data. Taking advantage of similar lobbying disclosure rules in the US and Canada, we create a cross-country lobbying dataset. We focus on the United States-Mexico-Canada Agreement (USMCA) to control for timing, salience, and issue scope. This helps us attribute differences in firm mobilization and trade lobbying strategies across the two countries to differences in political institutions. Strikingly different USMCA lobbying patterns emerge. Within the same industry, trade associations, the executive branch, and in-house lobbyists play a larger role in Canada. Meanwhile, well-established determinants of US lobbying fail to explain patterns of mobilization and the use of external lobbyists in Canada. These findings provide insights into comparative lobbying studies and indicate that some stylized facts about lobbying are unique features of the US political system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".