The Rival Wears Prada: Luxury Consumption as a Female Competition Strategy
Bibliographic record
Abstract
Previous studies on luxury consumption demonstrated that men spend large sums of money on luxury brands to signal their mate value to women and, thus, increase their reproductive success. Although women also spend copious amounts of money on luxuries, research focusing on women's motives for luxury consumption is rather scarce. Relying on costly signaling and intrasexual competition theory, the goal of the current study was to test whether female intrasexual competition in a mate attraction context triggers women's spending on luxuries. The results of the first experiment reveal that an intrasexual competition context enhances women's preferences for attractiveness enhancing, but not for non-attractiveness related luxuries such as a smartphone. This finding indicates that women may use luxury consumption as a self-promotion strategy during within-sex competitions, as these luxuries improve their advantages against same-sex rivals for mates. A follow-up study shows that compared to women who do not consume luxuries, women who do so are perceived as more attractive, flirty, young, ambitious, sexy, and less loyal, mature and smart by other women. These results suggest that luxury consumption may provide information about a women's willingness to engage in sex, as well as her views about other women, and consequently, her success in intrasexual competitions.
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 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".