Wine for the Table: Self-Construal, Group Size, and Choice for Self and Others
Bibliographic record
Abstract
Abstract This research examines how consumers make unilateral decisions on behalf of the self and multiple others, in situations where the chosen option will be shared and consumed jointly by the group—for instance, choosing wine for the table. Results across six studies using three different choice contexts (wine, books, and movies) demonstrate that such choices are shaped by the decision-maker’s self-construal (independent vs. interdependent) and by the size of the group being chosen for (large vs. small). Specifically, we find that interdependent consumers consistently make choices that balance self and others’ preferences, regardless of group size. In contrast, the choices of independent consumers differ depending on group size: for smaller groups, independents make choices that balance self and others’ preferences, while for larger groups, they make choices that more strongly reflect their own preferences. Via mediation and moderation, the data show that differential attention to others underlies the combined effect of self-construal and group size on the joint consumption choices that consumers make for the self and others.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".