Brand Constellations and Homophily: The Effect on Target Attitude Evaluations and Interpersonal Attraction
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
The purpose of this paper was to explore the relationship between attitudes toward a brand constellation (a group of brands) and how people perceived the attitudes of the person who owns those brands, do similar brand preferences indicate similar attitudes? Which products and brands are the most impactful in influencing perceptions of attitudes? Findings suggested that clothing and phones were the product types that had the greatest impact on perceived attitudinal similarity, while certain brands from other categories of products were more influential. The current study moves the brand constellation literature forward because it explores a concrete example of a brand constellation and its impact for marketers as they select impactful products and brands with which to form partnerships. By knowing how brands work together to influence perceptions and which products are the most impactful, brands can do a better job of portraying their products in a way that is desirable for consumers.
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 | 0.003 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it