Extremely Partisan Samples Impact Perceptions of Political Group Beliefs
Bibliographic record
Abstract
Accurately inferring the beliefs of a partisan group (e.g. Democrats, Republicans) can be challenging when exposed to extremely partisan beliefs from that group. Across two studies (total N = 566), we tested whether people correct these inferences for sample bias when it was explicitly disclosed. Study 2 further assessed how much of this correction is deliberate. Participants read 12 statements that most members of a political party (Democrats or Republicans) generally agree with. They were shown how strongly five party members agreed with each statement. In the biased sample conditions, these five party members were selected from the top 10% most partisan members; this bias was either disclosed or undisclosed. In the unbiased sample condition, the five members were representatively sampled from the entire party. Then, participants estimated on average how much the entire party agreed with each statement, and the likelihood that party members of the same or opposing parties agreed with each other. Participants’ mean estimates from the biased sample conditions were higher than the unbiased sample condition but lower than the samples viewed, indicating an (insufficient) attempt to correct for sample bias. Corrections were largest when sample bias was disclosed. Overall accuracy was highest when participants viewed unbiased samples, though across conditions there appeared a general tendency to overestimate strength of partisan beliefs. Parties were perceived as more homogeneous when participants viewed biased samples, regardless of whether bias was disclosed or not. While awareness of hyperpartisan bias helps correct judgments, it may not eliminate overestimation, overconfidence, or inflated perceptions of party homogeneity.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".