The Functions of University of Leiden’s Malay Language Reading Materials Among Malaysian Children
Bibliographic record
Abstract
The discovery of special collection of Malay language children’s stories at University of Leiden, Netherlands proves that the traditional Malay community cares for appropriate reading material for children. Based on Teuku Iskandar's note in the Catalogue of Malay, Minangkabau, and South Sumatran Manuscripts in The Netherlands, there are many stories of children in the archipelago that are stored in University of Leiden’s Library, Netherlands. However, in this study the researchers only examine three stories in the Children's Story 26 May 1862 Collection. The stories in the collection do not have specific titles, only known as Story 1, Story 2 and Story 3 and 2 stories are contained in the collection of Parable stories. This disclosure is made because there is an opinion that the folklore today is hardly recognizable by the younger generation. This universal issue encourages the researchers to look into old Malay manuscripts and their relevance in the context of children today. Hence the objectives of this study are to classify and analyze folklore functions towards children. Hence, the method of the study involves 450 children randomly chosen according to the zones, the north, south, east and west zones of Peninsular Malaysia, as well as the Sabah and Sarawak zones. The findings show that there are still folk tales that have certain functions that can benefit children. The findings also show that children today are less vulnerable to the stories of their ancestral heritage.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".