Politicians' social contacts and their effects
Bibliographic record
Abstract
In the framework of this project, politicians in 13 countries will be surveyed about political representation (in a broad sense). The questions are diverse: about how politicians evaluate different types of public opinion signals, about politicians’ perceptions of political inequality, their opinion about mass media bias, their representational role perceptions, and so on. The countries involved in the study are: Australia, Belgium (Dutch-speaking and French-speaking regions analyzed separately), Canada, Czech Republic, Denmark, Germany, Israel, Luxembourg, the Netherlands, Norway, Portugal, Sweden, and Switzerland. This preregistration deals with one specific, experimental module of the project. The module builds on work about the socio-economic background of politicians, showing that politicians predominantly belong to—and have contact with—the better-off classes in society (see e.g. Carnes & Lupu, 2015). Research has suggested that this explains (at least in part) why the preferences of advantaged societal groups are represented better in political decision-making than the preferences of the disadvantaged: the preferences of better-off groups are more top of mind for politicians as a result of the skew in their personal background and social circles. Based on these ideas, in this module, we run a survey experiment where politicians are cued to think about their rich or poor social contacts (or no contacts at all). More concretely, politicians in the treatment groups are asked to enter the initials of three social contacts (either poor or rich). In the control group, politicians are not asked any question about their contacts at all. After the treatment, we assess politicians’ preferences regarding two socio-economic policy proposals. Thus, we test how the priming of social contacts with a specific background affects politicians’ political position-taking, which allows us to reason about what would happen if politicians had a more diverse social circle and, thus, if poorer citizens were more top-of-mind for them. As a test of the skew in politicians’ actual social class environment, we additionally test how easily contacts from the different groups come to mind (via time stamps), and we ask about what type of relation politicians have with their contacts from these groups.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.002 |
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".