Replication Data for: When do politicians pursue more policy information?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When do politicians seek out expert information on policy? In this paper we explore whether elected officials seek out more information about an issue when they are farther offside the average opinion in their district on that issue. We designed and implemented a field experiment among Canadian Members of Parliament (MPs). In the midst of a contentious national debate on federal government support for the oil industry, we invited MPs and their staff to watch a webinar or read a written summary of the webinar. The webinar contained a variety of expert viewpoints on the future prospects of oil extraction in Canada. Some MPs were randomly assigned to information about the distribution of opinion in their constituency on the issue of whether the government should be involved in actively helping the resource sector, including in the construction of pipelines. We estimate the effect of receiving this district opinion on an MP seeking out expert knowledge in the form of the webinar. We particularly focus on the degree to which opinion disagrees with a politician’s party position. We find that politicians who are offside their constituency opinion do not appear more likely to seek out expert information on contentious policy issues.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.027 |
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 it