OBGYNs of TikTok and the role of misinformation in diffractive knowledge production
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
Health misinformation on social media has largely been examined from a harms-focused perspective, with scholars seeking to identify what impacts misinformation has on public health and a popular focus on removing it from platforms. The act of debunking is one response wherein misinformation is corrected with knowledge from scientific sources. To date, little research exists examining how experts and the public engage with misinformation beyond a focus on harm. Using Karen Barad's concept of diffraction, we examine the iterative relationships between misinformation, obstetrician-gynaecologists (OBGYNs) and the educational content they generate on the short-form video platform TikTok. Though misinformation and debunking content have been seen as oppositional, they are brought into productive dialogue with one another using diffractive techniques and platform affordances. We conclude that through the educational content created by the OBGYNs of TikTok, misinformation becomes diffractively integrated into debunking content and is generative of new knowledge, rather than cleansed away.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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