Key Learnings and Considerations for Utilizing Social Media Recruitment in Parasport Research
Why this work is in the frame
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Bibliographic record
Abstract
Despite the rise of digital methodologies in qualitative health and sports research (Goodyear & Bundon, 2020), there remains a gap in the usage of online methods in parasport populations. People with disabilities are often underrepresented in research as they are a traditionally hard-to-reach population due to accessibility limitations, stigmatization, and mistrust of researchers (Banas et al., 2019). The existing role of social media as a space for advocacy and social support in the parasport community (Bundon & Clarke, 2014) makes social platforms a potential tool for qualitative parasport research. Project Echo looks to leverage social media as a participant recruitment tool in parasport populations by informing recruitment strategies with the social model of disability and by placing an emphasis on collaboration to avoid the historical medicalization and marginalization of participants with disabilities in research. This article draws on the experiences of social media recruitment from Project Echo and aims to inform researchers looking to utilize social media as a research tool in parasport populations with key learnings and considerations. Banas, J. R., Magasi, S., The, K., & Victorson, D. E. (2019). Recruiting and Retaining People With Disabilities for Qualitative Health Research: Challenges and Solutions. Qualitative Health Research, 29(7), 1056–1064. https://doi.org/10.1177/1049732319833361 Bundon, A., & Hurd Clarke, L. (2014). Unless you go online you are on your own: Blogging as a bridge in para-sport. Disability & Society, 30(2), 185–198. https://doi.org/10.1080/09687599.2014.973477 Goodyear, V., & Bundon, A. (2020). Contemporary digital qualitative research in sport, exercise and health: Introduction. Qualitative Research in Sport, Exercise and Health, 13, 1–10. https://doi.org/10.1080/2159676X.2020.1854836
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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