“It’s Not Just Fashion For Fashion’s Sake”: Sustainable Fashion Social Media Influencer Ecosystem
Bibliographic record
Abstract
Globally we are witnessing the environmental demise of our planet. Simultaneously, consumers have shown a greater interest in shopping second-hand and the sustainable fashion industry is experiencing rapid growth, which is estimated to reach $8.25 billion by 2031 (Businesswire, 2020). This market acceleration led to the exploration of sustainable fashion social media influencers. Using semi-structured interviews with 20 sustainable fashion social media influencers, the research analyzes the ecosystem of sustainable fashion social media influencers and makes three unique contributions. First, the research introduces a three-part typology of sustainable fashion social media influencers: 1) sustainable lifestyle influencers, 2) sustainability influencers, and 3) thrifting influencers. Second, the research uncovers how sustainable fashion social media influencers perform vulnerability and sustainability failures in a strategy to portray curated authenticity. Finally, the research identifies "the entrepreneurial dichotomy," which refers to the challenge that sustainable fashion influencers face when they harmonize their ethos of sustainability and ethics along with their desire to leverage their social media platforms for profit.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".