Bibliographic record
Abstract
Malaysia is one of the multi-ethnic, multi-culture and multi-religious countries in Southeast Asia. Malaysia is a classic case where the state has used wide-ranging preferential policies to manage ethnic problems. As a matter of fact, because of the ethnic politics in Malaysia, the ethnic preferential policies affected most domains of this country, including social, political and economic areas, especially Chinese education in Malaysia. The objective of this paper is to examine Chinese education in Malaysia under Malaysian ethnic politics. Data of this article is based on two sources, primary data were collected through interviews and the informants were selected based on purposive sampling and snowball sampling, meanwhile, secondary data were collected from journal articles, newspapers, website pages and online resources. Hence, the authors focused on these qualitative data especially the informants’ oral interviews to reach the research objective. Content analysis was used to analyze primary and secondary data. Findings of this study indicate that, there is no doubt that the development of Chinese education in Malaysia is closely related to Malaysian ethnic politics, though it has undergone a thorny way, it is not a problem to maintain the status quo of the Chinese education; however, it is impossible to seek a great breakthrough at the present stage. Nowadays, the development of Chinese education in Malaysia depends to a certain extent on the development of China and it has a positive correlation with the Malaysia-China relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".