Bibliographic record
Abstract
• Research objectivesAnimal raw materials have long been closely associated with luxury. Although their use in this sector was rarely questioned until today, the situation has now changed and many companies are concerned that their image may be tarnished by animal abuse scandals. Under these conditions, could these materials be replaced by more responsible alternatives? This article proposes to provide some answers to this question.• MethodologyThis study uses a qualitative approach. Interviews were conducted in France, and involved 21 consumers and 13 professionals from the luxury sector.• ResultsThe analysis of the material obtained from interviews was based on the costly signaling theory and social value orientation, and reveals a reluctance regarding the use of alternative materials in luxury products. Materials of animal origin continue to be preferred because they correspond better to the consumer’s needs for quality and comfort or for ostentation.• Managerial/societal implicationsThe results help to identify the types of action that are necessary to: (1) continue to use animal raw materials while protecting the brand from negative associations, and (2) overcome sources of consumer reluctance to buy products made with alternative materials.• OriginalityThis is one of the very first studies on the subject of the use of animal skins and furs in the luxury sector. In this respect, it enriches the literature exploring the link between luxury and sustainable development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.899 | 0.810 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".