When TikTok Discovered the Human Remains Trade: A Case Study
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.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".