Politicians' evaluation of public opinion - module 3 - 2022 data collection
Bibliographic record
Abstract
In democracies, policies are expected to be responsive to public opinion. Extant research showed that responsiveness is selective. It varies across issues, time and countries. Yet, how come policies vary in their responsiveness has not received a satisfying answer. This project examines the puzzle of why policy responsiveness varies. Its core argument holds that politicians evaluate public opinion and let their actions—in line with public opinion or going against it—depend on their appraisal. When public opinion is evaluated negatively, it has no effect on what politicians do; that it is evaluated positively increases the chance that politicians act congruently. The project examines three matters: (1) which criteria politicians use to appraise public opinion; (2) how, depending on the opinion content of the message, the channel through which the opinion is conveyed and the group from which it comes, concrete public opinion signals are evaluated; and, (3) which effect these evaluations have on politicians’ political action. The central expectation is that public opinion is evaluated by politicians based on a consistent and common scoreboard. For instance, opinion signals are rated based on their representativity and underlying public opinion is evaluated on its quality and its intensity. The project tackles these matters drawing on a comparative study in fourteen countries (Australia, Belgium (both Flanders and Francophone Belgium), Canada, Czech Republic, Denmark, Germany, Israel, Luxembourg, Netherlands, Norway, Portugal, Switzerland, and Sweden). This registration relates to the first wave of data collection of *anonymous project*, to be carried out in 2022 and starting on March 21st, 2022. In this round, seating national and regional politicians in the fourteen countries will be surveyed and interviewed with regard to how they evaluate public opinion and the behavioral consequences thereof. There will be a second round of data gathering among politicians sometime in 2024 or 2025. A separate registration will be done for that second round of data. Note that the 2022 questionnaire contains a number of other questions, measures and experiments that will be registered separately. This registration only relates to the questions with regard to politicians' evaluation of public opinion.
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 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 | 0.024 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 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; both teacher heads agree on what is shown here.
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".