Multiple Routes to Self- versus Other-Expression in Consumer Choice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Studies of consumer decision making often begin with the identification of a dimension on which options differ, followed by an analysis of the factors that influence preferences along that dimension. Building on a conceptual analysis of a diverse set of problems, the authors identify a class of related consumers choices (e.g., extreme vs. compromise, hedonic vs. utilitarian, risky vs. safe) that can all be classified according to their levels of self- versus other-expression (or [un]conventionality). As shown in four studies, these problem types respond similarly to manipulations that trigger or suppress self-expression. Specifically, priming self-expression systematically increases the share of the self-expressive options across choice problems. Conversely, expecting to be evaluated decreases the share of the self-expressive options across the various choice dilemmas. In addition, priming risk seeking increases only the choice of risky gambles but not of other self-expressive options. These findings highlight the importance of seeking underlying shared features across different consumer choice problems, instead of treating each type in isolation.
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.
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 | 0.062 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it