Understanding MPs’ perceptions of party voters’ opinion in Western democracies
Bibliographic record
Abstract
The ability of Members of Parliament (MPs) to know the policy preferences of their party voters is a precondition for substantive representation. This study investigates whether MPs’ perceptions of their party voters’ opinions are more accurate with policy statements on which they are competent—namely, those that are owned by their party and in which the MPs have specialised. It combines unique data from citizen surveys and face-to-face meetings with 367 MPs in Belgium, Canada, Germany and Switzerland. Both citizens and MPs evaluated the same statements, and MPs also estimated the support for each statement among their party voters. The comparison of party voters’ preferences and MPs’ estimations shows that MPs are more accurate on statements owned by their party, but not on issues they themselves specialise in through committee membership. Party issue ownership plays an important role in determining MPs’ perceptual accuracy and, hence, democratic representation. Supplemental data for this article can be accessed online at: https://doi.org/10.1080/01402382.2021.1940647 .
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".