Across Enemy Lines: A Study of the All-party Groups in the Parliaments of Canada, Ontario, Scotland and the United Kingdom
Bibliographic record
Abstract
The Parliaments of Canada, Ontario, Scotland and the United Kingdom are now home to a growing number of informal bodies that are formed by politicians from all parties who wish to cooperate on specific policy issues or relations with other countries. Such all-party groups (APGs), which deal with topics from the steel industry to genocide prevention, work to share information, meet with stakeholders, and conduct policy studies. Most also have partnerships with external actors who support their activities. This dissertation explores why the number of APGs is rising in each jurisdiction, but also why there are relatively fewer APGs in the two Canadian cases. Using statistical analyses of APG membership patterns as well as interviews with parliamentarians, lobbyists, and journalists, it finds three main factors behind APG expansion. First, the growth of APGs has helped both parliamentarians and external actors to continue to achieve their goals despite changes in the external political environment such as rising policy complexity and increased demands from citizens. Second, APG expansion has been facilitated by the evolution and increasing modularity of the APG format, which has allowed it to be adapted to a broader range of issues and activities. Finally, the increasing acceptance of APGs as a standard tool of political advocacy has led to the creation of groups by parliamentarians and external actors who feel it is important do so as part of broader lobbying campaigns, even if APG activity is not the most effective way to achieve their goals. The dissertation also finds that differences in APG prevalence are caused primarily by variations in levels of party discipline, with those jurisdictions that feature high discipline, such as Canada, tending to have fewer groups. Strong party discipline also limits the policy advocacy conducted by those APGs that do form in those legislatures.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.042 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".