Bibliographic record
Abstract
Malaysia is the second highest mobile penetration in South East Asia after Singapore. Although M-Commerce still at infancy stage, Malaysia has already embarked on the adoption of M-Commerce. As the communications and multimedia industry evolves towards convergence, licences under the Communications and Multimedia Act 1998 are formulated to be both technology and service neutral. This creates opportunities for expansion into the industry particularly in the area of applications service providers and provides for a more effective utilisation of network infrastructure. To help future applications and technologies handle M-Commerce, Varshney and Vetter (2002) proposed four levels of M-Commerce framework: M-Commerce applications, user infrastructure, middleware, and network infrastructure. Mohd and Osman (2005) have adapting this model into M-Commerce applications in Malaysia. M-Commerce does have a bright future in Malaysia. To achieve this objective, mobile users expect an improvement in charges access fee, network quality, accessibility and speed. Other issues need to be considered are security and customer customisation. With government support, M-Commerce in Malaysia has a very promising future and moves forward in sectors that are clearly going to be the engines of growth worldwide over the next few years.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".