Gansang Stone Inscriptions: A New Discovery That May Change the History of the Tai-Kadai Ethnic Groups
Bibliographic record
Abstract
The discovery of the Gansang stone inscriptions is the most important ancient character discovery in China since that of the oracle-bone inscriptions. It has had a major impact on research on ancient characters in China, and it will also have serious consequences for the study of human civilization. The discovery makes it possible to rewrite the history of the ancient Tai-Kadai ethnic groups in Southwest China, which were previously thought to have no direct written history. Radiocarbon dating of the stone tablets indicates that the Gansang stone inscriptions have a history of about 3,000 years. Scholars agree that the Gansang stone inscriptions display an ancient ideographic writing system of the ancient Tai-Kadai ethnic groups and that they date to almost the same era as the oracle-bone inscriptions. While the position of stroke movements in the inscriptions has been determined, it is unclear whether the texts are arranged from left to right or right to left. A comparative analysis of the Gansang stone inscriptions, the oracle-bone inscriptions, the Shuishu writing system, and the ancient Yi writing system indicates that the Gansang stone inscriptions recorded people’s apparel, architecture, residence, eating habits, transportation, hunting activities, war, raising livestock, sacrifice, divination, astronomy, and calendar.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".