Exploring the Role of Resources in Ethnic Minorities’ Adoption of Information and Communication Technology in Preserving Their Cultural Identity in Malaysia
Bibliographic record
Abstract
The aim of this article is to investigate the current conditions of the adoption of Information and Communication Technology (ICT) in preserving cultural identity among the ethnic minorities in Peninsular Malaysia. ICT plays a crucial role in the present knowledge-based globalization era and the usage has become a basic necessity for all members of the society in managing their daily lives. It has pervaded every aspect of human life and has significantly changed the manner in which society communicate and interact with one another. This is particularly important as the ethnic minorities in Malaysia inevitably facing the great challenges of losing their cultural identity through the assimilation process into the larger mainstream society. ICT appear as an alternative which help in enhancing the efforts for cultural preservation. The penetration of ICT in ethnic minority everyday life has directly or indirectly provides a platform for them to express and share their ideas, thoughts, perceptions and knowledge about their existing culture. However, are the ethnic minorities have sufficient resources to adopt ICT to preserve their culture is still unexplored. The extent of the ethnic minorities' awareness of the advantages of ICT to preserve their cultural identity is still questionable.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".