Interest Groups and Parliamentary Committees: Leveling the Playing Field
Bibliographic record
Abstract
Parliamentary committees in Canada are undeniably important resources for interest groups – particularly in terms of gaining information, articulating one’s message on public record, and establishing oneself as a legitimate stakeholder in the eyes of politicians, government and the public. However, one of the intended functions of standing committees — to serve as a venue for non-governmental influence on policy — has largely proved to be a canard in Canada’s House of Commons. The first part of this article prioritizes the challenges facing non-governmental actors who wish to exert policy influence through parliamentary committees. It asserts that standing committees’ function of carrying out studies has more surmountable challenges than the function of legislative reviews. The second part of the article emphasizes that two developments are imperative in order to realize the potential committee studies hold: first, the open-ended nature of studies and the inadequacy of follow-up mechanisms should be addressed (with the scope of questions designed to feed into a pipeline of future legislative activity wherever feasible), and second, long-overdue accountability mechanisms should be introduced to ensure that the government responds to committee reports upon request.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".