“There’s A Little Bit of That Magic Where I’m Becoming Something Else”: LGBT+ Furry Identity Formation and Belonging Online
Bibliographic record
Abstract
Active and open identi cation with animals and the creation of anthropomorphic (or zoomorphic) fursonas have become infamous on the internet, yet published research on the subculture is lacking. This ethnographic study explores this under-examined subculture by considering how individuals who identify as LGBT+ and as furry nd and experience community online in ways that contribute to feelings of belonging, inclusion, and overall well-being. Through a series of semi-structured interviews, it was found that participating LGBT+ furries experienced an increase in self-reported emotional well-being when allowed to engage with online furry fandom. Given that furry identity is inherently non-normative, the fandom becomes an accepting space for other non-normative identities, like LGBT+ identities. By creating accepting online communities, those without access to supportive communities in their o -line lives can learn about and explore non-normative identity without judgement. These spaces may allow for the accumulation of multiple non-normative identities, all of which are in relative harmony within online furry fandom, which serves as a “catch-all” identity. Existing within this space had positive a ects on the well-being of the participants.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".