Visual Influence and Social Groups
Bibliographic record
Abstract
New car purchases are among the largest and most expensive purchases consumers ever make. While functional and economic concerns are important, the authors examine whether visual influence also plays a role. Using a hierarchical Bayesian probability model and data on 1.6 million new cars sold over nine years, they examine how visual influence affects purchase volume, focusing on three questions: Are people more likely to buy a new car if others around them have recently done so? Are these effects moderated by visibility, the ease of seeing others’ behavior? Do they vary according to the identity (e.g., gender) of prior purchasers and the identity relevance of vehicle type? The authors perform an extensive set of tests to rule out alternatives to visual influence and find that visual effects are (1) present (one additional purchase for approximately every seven prior purchases), (2) larger in areas where others’ behavior should be more visible (i.e., more people commute in car-visible ways), (3) stronger for prior purchases by men than by women in male-oriented vehicle types, (4) extant only for cars of similar price tiers, and (5) subject to saturation effects.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".