ANNALS OF THE UNIVERSITY OF ORADEA FASCICLE OF TEXTILES, LEATHERWORK 133 DETERMINANTS TO THE CONSUMPTION OF LEATHER PRODUCTS
Bibliographic record
Abstract
The current paper looks at some of the determinants in the consumption of leather fashion products by the 16-34 year old population of Québec. Although the Québec population presents some idiosyncrasies, we surmise that our results could probably be generalized to similarly aged population groups in the western world. Leather goods are now often considered as luxury it ems, a sub-sector of the fashion industry which has been outgrowing all others in the last few years. Yet leather is often seen as non-ethical, requiring the killing of animals, and non-ecological, since the production process is polluting. Those two opposing forces make the future of leather very uncertain. Surprisingly, very little literature has been written on the subject. This paper presents a survey in which the authors tried to identify the determinants to the consumption of leather in the 16-34 age groups, often referred to as Generation Y. Our findings reflect this dichotomy between the consumer’s interest for apparel and his or her negative reaction to some characteristics of leather. Although not as influenced as fur by ethical and ecological concerns, leather products the object of similar reactions. This paper concludes on some recommendations to participants in the leather industry who should note this trend and try to position their products in such a way as to minimize the negative elements and bank on the more positive aspects.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.227 | 0.047 |
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".