Buy One Get One: The Legal and Socio-Cultural Context of ‘Gifting’ Within the Australian Human Remains Trade
Bibliographic record
Abstract
Today’s global human remains trade – how it operates on and offline, where remains come from, and how algorithmic amplification allows for complex networks to form between buyers, sellers, and middlemen – has seen an increasing amount of research and media attention. Underpinning this increasing interest is the growing realization that poorly regulated trafficking inflicts genuine psychological harm on the living (whether relatives of body donors or descendant communities), as well as accrues losses to the archaeological record or risks the jeopardization of crime scenes. Much of this work, however, has focused on the global north. Within the global south, Australia is recognized as an emerging market country for many categories of cultural heritage trafficking, including human remains. This paper reviews the function and socio-legal context of a specific seller’s tactic so far seen only among Australian human remains collectors, whereby photographs of human remains are offered for sale, with the bones themselves included as a “gift”. From a network analysis of text from a corpus of anonymized posts from Facebook, conducted using t-SNE and Voyant Tools, 11 key discourse themes are identified that point to how and why this sales tactic is used. Better understanding its function is a necessary first step to closing this loophole within Australian law, but also to identifying similar tricks at work within collector networks elsewhere.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".