Representation and Partisanship: What Determines the Topics That Members of Parliament Prioritize in Communications with Their Constituents?
Bibliographic record
Abstract
Abstract What determines how Members of Parliament (MPs) and their staff frame their communications with all constituents in their electoral district? Prior research has suggested that constituency operations are one of the last bastions of freedom that MPs have from the full grasp of party discipline in Canada. If this remains true, MP communications with their constituents should reflect the MPs’ background or the constituency context and not their political partisanship. We collected a sample of published newsletters (“householders”) that Canadian MPs’ offices sent to all households in their electoral districts during the COVID-19 pandemic. We supplement our analysis with original insights about householders from a selection of MPs and their staff. Our results suggest that in a system of strict party discipline, the most important predictor of what MPs include in their constituent communications is indeed partisanship. The results inform our understanding of democratic representation, centralized co-ordination and political communication, and the pervasiveness of partisan messaging in Canada.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".