Replication Data for: Willems, E., Maes, B. & Walgrave, S. (2024) Mechanisms of political responsiveness: The information sources shaping elected representatives’ policy actions. Political Research Quarterly
Bibliographic record
Abstract
This study examines the micro-level foundations of how policy responsiveness may come about. Our study builds on the assumption that elected officials’ information source use shapes their policy actions. We analyze the variation in information sources elected officials rely on for agenda-setting and policy formulation, distinguishing between public opinion sources, advocacy sources, and expert sources. Additionally, we examine how elected officials’ public opinion sources vary across individuals, parties, and political systems. Based on a 2015 survey with 345 Members of Parliament in Belgium and Canada, the results indicate that the actions of elected representatives are more affected by public opinion sources like citizens and the mass media when they initially prioritize issues for policy action, while interest groups are prominent in both stages, and parties and expert sources are more used in the policy formulation phase. Furthermore, politicians in majoritarian systems, those belonging to the opposition and members of populist parties, tend to rely more on public opinion sources than their peers in proportional systems, those in the majority and non-populist parties.
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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.251 | 0.206 |
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".