Perception of non-binary social media users towards authentic non-binary social media influencers
Bibliographic record
Abstract
This paper explores an authentic way for brands to connect with the non-binary community, an understudied and underserved audience. With a call for better representation, this study is the first to investigate what role non-binary social media influencers (SMIs) may play in filling this gap. Using Interpretative Phenomenological Analysis, non-binary social media users were interviewed on their perceptions, thoughts, and feelings of non-binary SMIs. Three superordinate themes were discovered: (1) Motivations for following non-binary SMIs, (2) Popularity Factors of non-binary SMIs, and (3) Representation of the community through non-binary SMIs. The findings may be paired with existing literature to provide a basis for future research on influencer marketing to the non-binary community. • This is the first study to examine the marginalized group of non-binary consumers perceptions of non-binary social media influencers. • Interpretative Phenomenological Analysis was used to analyze the lived experiences of 13 non-binary individuals. • The superordinate themes of motivations, popularity and representation were identified, and these were rooted in twelve subordinate themes. • Non-binary social media influencers who acknowledge their intersectionality are the most popular. • The research advances our knowledge of the perceived authenticity of non-binary social medi a influencers an under researched and marginalized group.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".