Digital Inclusion and Lifestyle Transformation among the Orang Asli: Sacrificing Culture for Modernity?
Bibliographic record
Abstract
In the Digital Era, being part of the digital society is no longer an option particularly for those living in the urban areas. Caught by the e-wave and the onslaught of sophisticated information and communication technologies (ICT), most urbanites are e-savvy unlike those living in rural locations, particularly the indigenous groups. Is there a need for simple, rural folks to embrace digital literacy and be e-inclusive? Hence, the objective of the study is to assess the level of literacy and computer literacy amongst the indigenous people or natives living in a rural area of Perak, Malaysia. Cross-sectional research design with purposive sampling was employed and the instrument used was a survey form. The findings revealed that 30.8% of the respondents were illiterate and only 5.2% who were computer literate thus, substantiating the myth of digital inclusion among the minorities. With the government’s transformation plan to have connected citizens through broadband access, the dilemma was the motivation for this research and inherently, substantiated. Although native minorities in Perak, Malaysia formed the sample size for this study, the implications provide justification for policy analysis on socio-technological inclusion among other disadvantaged groups as culture remains strongly ingrained in their every day existence. However, with time, the new generation may revolutionize the outlook of the indigenous group towards modernity and ICT. A change champion together with a positive, political environment would retard the myth and rhetoricism in promoting e-access for social inclusion and citizen development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".