Preserving Cham Font through Online Conversion Application
Bibliographic record
Abstract
The Cham people who are now the minority ethnic in Vietnam speaks with the language familiar used as others Malay groups but differ in their written language. Cham Script inscriptions appear on Dong Yen Chau stone stele (Tra Kieu) in 4th century and the Cham are using this script system until today. Ensuring the preservation of Cham language, this study intended to design a tool to convert the EFEO Cham Latin in Malay system to Cham Akhar Thrah. The method used is by converting Latin EFEO into intermediate characters code followed by assigning it to Akhar Thrah backwards. Cham font conversion application has been created, which has carried out a number of technical requirements, and content conversion ensures correct in vocabulary, semantics and grammar. In this experiment we have checked the accuracy percentage of three Cham poems and results Ariya Cam Bini 100% (n=1823); Ariya Gleng Anak 99.88% (n=2459); Nai Mai Mang Makah 100% (n=2523). Cham font conversion is necessary and meaningful in conservation of Cham script. It will be used in schools, institutions in the country and overseas as well as assist in teaching and learning Cham language.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".