Sustainable fashion social media influencers and content creation calibration
Bibliographic record
Abstract
Given the rise of social media, social media influencers have become an essential part of marketing agencies’ strategies. Advertisers seek to leverage influencers’ large community of followers who place trust in influencers’ recommendations. This trust makes the use of influencer marketing a powerful tool for advertisers. With increasing consumer interest, the sustainable fashion industry has grown and social media influencers are being leveraged to shift consumer perspective and purchasing behavior. Using semi-structured interviews, this research addresses the use of influencers as an advertising tactic in the sustainable fashion industry to analyze the social media practices and monetization strategies of sustainable fashion social media influencers.The term ‘sustainable fashion social media influencers’ is introduced to describe influential content creators who discuss sustainable fashion on social media. Importantly, the research identifies ‘content creation calibration’, which refers to the practice of social media influencers calibrating their content to account for their ethics and desire for compensation. The research highlights the future challenges for advertisers and influencers when linking sustainability to entrepreneurship in influencer marketing.
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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".