Understanding knowledge effects on attitude-behavior consistency: The role of relevance, complexity, and amount of knowledge.
Bibliographic record
Abstract
The role of properties of attitude-relevant knowledge in attitude- behavior consistency was explored in 3 experiments. In Experiment 1, attitudes based on behaviorally relevant knowledge predicted behavior better than attitudes based on low-relevance knowledge, especially when people had time to deliberate. Relevance, complexity, and amount of knowledge were investigated in Experiment 2. It was found that complexity increased attitude- behavior consistency when knowledge was of low-behavioral relevance. Under high-behavioral relevance, attitudes predicted behavior well regardless of complexity. Amount of knowledge had no effect on attitude- behavior consistency. In Experiment 3, the findings of Experiment 2 were replicated, and the complexity effect was extended to behaviors of ambiguous relevance. Together, these experiments support an attitude inference perspective, which holds that under high deliberation conditions, people consider the behavioral relevance and dimensional complexity of knowledge underlying their attitudes before deciding to act on them.
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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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".