The work of Canadian political staffers in parliamentary caucus research offices
Bibliographic record
Abstract
Abstract Since 1970, recognized political parties in the Canadian House of Commons have received funding for caucus research offices. Staffed by political partisans, research offices provide policy, communications, research and administrative support to party leaders and their parliamentary caucuses. This research note examines the evolving organization, work and function of these offices. It demonstrates, first, that the tendency towards centralization, evident in Canadian politics for decades, is clearly reflected in research offices’ primary support for leaders rather than individual caucus members. Second, research offices are integral to parties’ strategic communications and marketing efforts, and this, especially in government, often eclipses their policy contribution. Third, while the government party views caucus researchers as a useful supplement to public service and ministerial office resources, opposition parties rely heavily on their caucus research offices as their dominant source of staff capacity.
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.058 | 0.016 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".