Perception of non-binary social media users towards authentic non-binary social media influencers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it