How Fashionable Are We? Validating the Fashion Interest Scale through Multivariate Statistics
Bibliographic record
Abstract
A person’s fashion interest describes how familiar a person is with fashion. There are major differences among consumers in terms of fashion interest that can be used as a segmentation criterion for markets. Understanding the drivers of clothing consumption can be used to develop strategies to address consumption habits, including overconsumption. Consequently, many studies have developed questionnaires and interview guidelines to define fashion interest or other fashion-related attitudes and behaviors. However, there is a gap in research about validating fashion scales. This study validates a fashion interest scale by comparing a random sample with a control group of fashion students, demonstrating differentiation between groups. We used principal component analysis (PCA) to explore the scale’s homogeneity and t-tests and analysis of variance (ANOVA) to validate the scale. The results suggest that the scale is homogeneous and has high validity. We conclude that the scale can be used as a tool to segment markets to gain faster and higher quality data and as a benchmark for other studies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".