Reflecting on Six Decades of Selective Exposure Research: Progress, Challenges, and Opportunities
Bibliographic record
Abstract
Abstract For over 60 years, researchers have explored the validity of the selective exposure hypothesis, which states that people will seek out consonant, and avoid dissonant, information. In early cognitive dissonance‐based research, selective exposure received mixed support. More recently, researchers have begun to delineate the factors that regulate the occurrence of selective exposure in a multitude of contexts. In this review, we discuss a number of such moderators as well as the ebb and flow of research over the years. We propose that many of these factors can be conceptualized as influencing capacity and/or motivations to process information, and we discuss how this framework can help categorize past, and suggest future, moderators. Finally, we highlight that other research domains should be considered when exploring selective exposure effects, and that researchers should consider how findings from the selective exposure literature can fruitfully be applied to other domains.
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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 | 0.095 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.014 | 0.035 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".