Seeding the Grassroots of Research on Furries: Lessons Learned from 15 Years of Creative Knowledge Mobilization, Valuing Community Partnerships, and Correcting the Record on Stigmatized Communities with Evidence-Based Scholarship
Bibliographic record
Abstract
This paper documents a case study of how academics can use traditional research and non-traditional knowledge mobilization to improve the dissemination of findings related to stigmatized communities. The International Anthropomorphic Research Project (IARP) used peer-reviewed scholarship to challenge pervasive media misconceptions and misinformation about furries. Finding the reach of traditional academic outlets was inadequate to meaningfully impact mainstream misconceptions, we rebranded our research efforts under the name Furscience and utilized social marketing and creative dissemination to repackage the IARP’s research into more public-friendly, accessible formats. Furscience has become a multi-purpose platform specifically engineered to forge connections among academics, furries, the public, and media. It also supports the furry community’s own diverse, anti-stigma efforts by providing data, public education, and partnerships. We offer preliminary evidence that suggests Furscience has increased its public reach and that furries, themselves, see improvements in how the media and public understand their community. This case study offers academics who work with stigmatized populations—especially those plagued by misinformation—and engage in translational research an example of how data, community and media partnerships, and non-traditional dissemination strategies can improve research accessibility and anti-stigma efforts. We conclude with a summary of the lessons learned by Furscience.
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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 | 0.190 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.017 | 0.081 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.008 | 0.011 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".