Behind Closed Doors: The Human Remains Trade within Private Facebook Groups
Bibliographic record
Abstract
The existence of a thriving trade in human remains online is facilitated by social media platforms. While much of this trade is conducted in fully public forums such as e-commerce platforms, the retail website of bricks-and-mortar stores, public personal and business pages on social media, etc., there also exist numerous private groups using the affordances of various social media platforms to buy, sell, and share photographs of human remains. This article describes a case study of four private Facebook groups featuring people who buy and sell human remains, to explore how the discourses of the trade may be different when not made in public. Using a close-reading approach on the text of posts and threaded conversations, and associated visual similarity analysis of the accompanying photographs, we observe, among other things, a strikingly 'more professional' approach, shibboleths and patterns of behaviour that serve to create group identities. We analyse posts made over a seven-week period across the selected private groups in the run-up to the 2023 holiday season. Given the issues of privacy raised by studying private groups, we also experiment with a locally hosted large language model to see if it could classify discourses meaningfully without the intervention of a researcher having to read the original posts. This case study might also serve as a model for other kinds of research investigating the reception of various archaeological topics that might be discussed and understood differently in private versus public venues.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".