Bibliographic record
Abstract
Little work has been done recently on the study of House of Commons committees or their influence in Canada [Brodie, I. (2018). At the centre of government: The Prime Minister and the limits on political power. McGill-Queen’s University Press; Stilborn, J. (2014). The investigative role of Canada’s House Committees: Expectations met? The Journal of Legislative Studies, 20(3), 342–359. https://doi.org/10.1080/13572334.2014.890801], despite repeated calls to reform committees in response to improving Canada’s democracy functioning [Chong, M. (2017). Rebalancing power in Ottawa: Committee reform. In M. Chong, S. Simms, & K. Stewart (Eds.), In Turning parliament inside out: Practical ideas for reforming Canada’s democracy (pp. 80–97). Douglas & McIntyre]. Anecdotal evidence, however, particularly interviews given by current and former MPs, indicates that committees are sources of influence in the Canadian political system. This paper seeks to shed light on this lacuna by examining amendments to government bills by House of Commons standing and legislative committees from 2004 to 2019. This study concludes that committees are, in fact, a source of systematic, substantive influence on government legislation. Committees, therefore, deserve much more academic attention.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".