Understanding When Similarity-Induced Affective Attraction Predicts Willingness to Affiliate: An Attitude Strength Perspective
Bibliographic record
Abstract
Individuals reliably feel more attracted to those with whom they share similar attitudes. However, this affective liking does not always predict affiliative behavior, such as pursuing a friendship. The present research examined factors that influence the extent to which similarity-based affective attraction increases willingness to affiliate (i.e., behavioral attraction) – one potential step toward engaging in affiliative behavior. Research on attitude strength has identified attitude properties, such as confidence, that predict when an attitude is likely to impact relevant outcomes. We propose that when one’s attitudes possess these attitude strength-related properties, affective attraction to those who share that attitude will be more likely to spark willingness to affiliate. Across four studies on a variety of topics, participants (N = 428) reported their attitudes and various attitude properties regarding a topic. They were introduced to a target and learned the target’s stance on the issue. Participants reported their affective attraction and willingness to pursue friendship with the target. Consistent with past research, attitude similarity predicted affective attraction. More importantly, the relation between affective attraction and willingness to affiliate with the target was moderated by the attitude-strength related properties. A mini meta-analysis found this effect to be consistent across the 4 studies.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".