When TikTok Discovered the Human Remains Trade: A Case Study
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
Abstract In the summer of 2021, a video on TikTok was heavily reposted across a variety of social media platforms (attracting conventional media attention too). Unusually (for TikTok), it was about the trade in human remains. Thus, we were presented with the opportunity to watch how knowledge of the trade exploded into broader public consciousness on a comparatively newer platform. In this article, we scrape TikTok for reactions to that moment. In our previous research on the human remains trade on Instagram, we used a particular suite of digital humanities methods to understand how Instagram was being used by participants in the trade. Here, we employ those same methods to develop a case study for contrast. The original individual, whose TikTok account is used to promote his bricks-and-mortar business buying and selling human remains, has, as a result of this attention, gained an even greater number of followers and views, making the video a “success.” Nevertheless, several users engaged in long discussions in the comments concerning the ethics of what this individual is doing. A number of users created videos to criticize his activities, discussing the moral, ethical, and legal issues surrounding the trade in human remains, which in many ways makes the “success” of this video one of fostering opposition and a wider understanding of the ethical and moral issues around this trade.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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