Geographic, Substantive, and Descriptive Representation Through the Lens of the Represented
Bibliographic record
Abstract
A central question in the study of democracy is how different types of political representation affect the performance of democratic systems and, in turn, support for democracy. We contribute to this question by investigating the structure of preferences over geographic, substantive, and descriptive representation through the lens of citizens. We explore both mass preferences and beliefs using data from surveys fielded to samples of the adult population in Australia, Canada, Mexico, Portugal, Spain, Switzerland, Turkey, and the US. Our main evidence is based on a conjoint-experimental design that presents respondents with scenarios varying the level of over- and under-representation along geographic, substantive, and descriptive dimensions. By analyzing how changes in representational inequality drives both support for a scenario and agreement with a series of statements about public policy that followed each conjoint task, we identify whether preferences over representation type mirror diverging beliefs about economic performance, redistribution, and legislative outcomes. We also investigate how the representativeness of legislative bodies affects normative beliefs about the extent to which citizens prefer principled or preference-based representation, i.e., the extent to which policymakers should make decisions based on their own values and beliefs instead of responding to shifts in 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 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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".