The Advocacy Coalition Framework and Nascent Subsystems: Trade Union Disclosure Policy in <scp>C</scp>anada
Bibliographic record
Abstract
This article examines the Advocacy Coalition Framework (ACF) in the context of a nascent policy subsystem with a longevity of less than 10 years. It evaluates key aspects of the model in a recent area of Canadian national policymaking, namely the attempt to impose greater reporting and disclosure requirements on trade unions through Bill C‐377. Following the ACF's prediction of a correspondence between policy belief systems and coordinated advocacy, the article identifies ideological groupings of advocates in this policy area—defined here as advocacy communities—and examines the level of coordination within and between them. The results show that advocacy coalitions emerged rapidly in this subsystem and corroborate the link between coordination and policy core beliefs. The article provides two qualifications. First, when there are multiple advocacy communities, rather than a simple dichotomy, the relationship between beliefs and coordination is weakened. Second, linkages across different advocacy communities were more prevalent with lower level forms of coordination, such as exchanges of information, than they were with higher level activities. The study is based on a content analysis of briefs and testimonies to two parliamentary committees and a mailed questionnaire to organizational representatives advocating on this issue.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".