Palmer River Goldfield Chinese Coin Hoard: New Evidence Challenging Its Authenticity
Bibliographic record
Abstract
This paper investigates the widely publicised claim by Keith Courtenay in the late 1970s that he had found a large hoard of 32,000 Chinese ©cash© (Chinese coins with a square hole in the middle) in the Palmer River Goldfield in far north Queensland, Australia. The discovery of the hoard was a momentous event at the time, but almost immediately some researchers raised reservations about its authenticity because of inconsistencies in Courtenay©s accounts of the circumstances that led to its discovery and its immense size in terms of the number of the coins, far greater than any other find of Chinese coins in any overseas Chinese context. Our research reviews all the evidence relating to the discovery and publicity about the hoard at the time, the people involved, and the subsequent sale and gifting of large portions of it. We conclude that while the coins are genuine Chinese cash, there is little likelihood, partly based on the young age of some of the coins, that they were found in the Palmer Goldfield as alleged. We outline a more likely scenario about how they were acquired along with evidence to support our conclusions. At the time, most people had no reason to think the hoard was not genuine and the story of its discovery and sale were uncritically integrated into local histories and remain so to this day
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".