A Subjective Measure of Attitude Extremity: Validation and Extension
Bibliographic record
Abstract
Attitudes are individuals’ enduring global evaluations of objects, concepts, or ideas, which may vary in both valence and extremity. There are many important determinants of attitudes and attitude strength, with research suggesting that one important determinant is the extremity of one’s attitude. The present research explored two possible methods of capturing individuals’ attitude extremity through objective and subjective measures. The goal of the current research was therefore to test two propositions: First, that the measures should be largely independent of one another, and second, that each measure would tap into meaningful processes of the functioning of the attitude. Study one examined the role of objective and subjectively measured attitude extremity on resistance to persuasion attempts. Here, we found that the measures were modestly correlated to one another, and that both exerted independent and opposite effects on persuasion resistance. Study two examined the role of subjective and objectively measured extremity on information processing. We again found modest correlations between the measures, though each measure produced minimal effects on information processing. The exception to this was our finding that individuals’ attitudes affected the favorability of their thoughts, and that this effect was more pronounced for objectively extreme attitudes. We discuss implications for this research in the context of the broader attitude strength literature and future directions for this work.
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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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".