Feeling Conflicted and Seeking Information
Bibliographic record
Abstract
To date, little research has examined the impact of attitudinal ambivalence on attitude-congruent selective exposure. Past research would suggest that strong/univalent rather than weak/ambivalent attitudes should be more predictive of proattitudinal information seeking. Although ambivalent attitude structure might weaken the attitude's effect on seeking proattitudinal information, we believe that conflicted attitudes might also motivate attitude-congruent selective exposure because proattitudinal information should be effective in reducing ambivalence. Two studies provide evidence that the effects of ambivalence on information choices depend on amount of issue knowledge. That is, ambivalence motivates attitude-consistent exposure when issue knowledge is relatively low because less familiar information is perceived to be effective at reducing ambivalence. Conversely, when knowledge is relatively high, more unambivalent (univalent) attitudes predicted attitude-consistent information seeking.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".