Ethical fashion consumption: Market research and fashion sustainability in Canada and beyond
Bibliographic record
Abstract
Today’s consumers are very connected and knowledgeable and have very high expectations of businesses in terms of corporate social and environmental responsibilities. Yet, researchers have demonstrated the existence of a behaviour gap between consumers’ intention and their action. While they expect brands to be more responsible and are willing to pay more from the ones that ‘do good’, that willingness to purchase more ethical products fails to translate to a concrete purchase in reality. This behaviour gap is a real challenge to ethical fashion brands, a challenge that must be addressed in order to support the growth of the market and ensure that sustainability truly becomes the fashion industry framework. This chapter supports to a certain extent the arguments of the Professors Kate Fletcher (University of Arts London) and Lynda Grose (California College of Arts) in favour of creating longer-lasting garments through the design for sustainability approach. Their work has, among other things, put the user back to the centre of discussions and his relationship with clothing and how this relationship should affect systemic change in the industry. The purpose of this chapter is to bring the consumer behavioural paradox to light and to share through a case study one way ethical fashion brands could bridge consumers’ expectations and thus empower them in their choice to live more sustainably.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".