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.
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 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 | 0.025 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".