Interest Group Adaptations to Campaign Finance Reform in Canada and the United States
Bibliographic record
Abstract
Abstract.The United States and Canada enacted similar campaign finance reforms in the early 2000s. This article draws upon interviews with leaders of the major Canadian interest groups to explore similarities and differences in the responses of Canadian and American interest groups to reform. While groups in both countries shared an increased emphasis on mobilization and communication with members, the Canadian reforms were more effective at removing many groups from political campaigns entirely. This difference is primarily a result of differences in the two nations' party systems and the historical development of interest groups in the two countries. Résumé.Au début des années 2000, les États-Unis et le Canada ont promulgué des lois similaires visant la réforme du financement des campagnes électorales. Cet article est basé sur des entretiens avec les chefs des principaux groupes d'intérêt canadiens. Il étudie les ressemblances et les différences entre leurs réponses à ces réformes et celles des groupes d'intérêt américains. Même si les groupes des deux pays ont tous insisté sur la communication et la mobilisation de leurs membres, les réformes canadiennes ont mieux réussi à éliminer entièrement plusieurs groupes des campagnes électorales. Cette différence s'explique surtout par la structure différente des deux systèmes de partis politiques et par l'évolution historique des groupes d'intérêt dans ces deux pays.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".