Bibliographic record
Abstract
When discussing their political views, people tend to believe in their positions with a great deal of certainty while also misperceiving others’ certainty as indicating extremity. The current study strived to test whether people would tailor their expressed certainty when they believed they were interacting with someone who agreed with them, disagreed with them, or was neutral on the topic. Participants provided descriptions of their views on two political issues and were randomly assigned to describe their position as if they were to explain it to someone who shared their views, opposed their views, or had no opinion on the issue. Participants ultimately came across as quite certain when describing their political positions, regardless of whether they were communicating with an opponent or compatriot. Consequently, participants were more likely to state their ideas as fact and use language that enhanced the apparent certainty of their statements. Participants rarely tempered their certainty and often did not recognize opposing positions without also criticizing it, especially when faced with someone who disagreed. These findings suggest that people will not change how certainly they describe their views when given the motivation to do so. Faculty Mentor: Craig Blatz Department: Psychology
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".