Bibliographic record
Abstract
Both the theory of top-down penetration of fashion consumption (Veblen - Simmel model) and the theory of bottom-up fashion consumption have been found consistent with the consumer behavior in the China’s fashion consumer market and the trend of such behavior keeps growing. Therefore, it is necessary to carry out a study on fashion consumption to meet the needs of the development of real life and fashion consumption. Firstly, we describe the content of fashion consumption, discuss the connotation and main features of fashion and analyze the relationship among fashion, innovation, classic and prevalence; namely, innovation is the pioneer of fashion while the classic is the denotation of faded fashion and prevalence is the continuance of fashion. Secondly, we examine the psychological motivations and behavioral mechanisms of fashion consumption, pointing out that the psychological motivations of fashion consumption is showing-off and it is of symbolic significance. The general characteristics of fashion buyers and consumer characteristics including the elegant fashion, the popular fashion, the cool fashion and the Bobos fashion are presented. We put forward a decision-making process in fashion consumption according to the influence of fashion, the extent of consumer involvement and the purchase decision time. Finally, by analyzing the fashion consumption behavior of the Chinese consumers, we point out that the core of the cool fashion or the Bobo fashion is actually not materialism and both cool fashion and Bobo fashion will have a profound influence in the fashion consumption in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".