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Record W2170117536 · doi:10.5539/ass.v9n6p9

Tracing the Diffusion of Internet in Malaysia: Then and Now

2013· article· en· W2170117536 on OpenAlexvenueno aff
Ali Salman, Er Ah Choy, Wan Amizah Wan Mahmud, Roslina Abdul Latif

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetInternet accessBusinessPopulationTelecommunicationsAdvertisingWorld Wide WebComputer scienceSociologyDemography

Abstract

fetched live from OpenAlex

The Internet has brought about a huge change in the way we do things and on many aspects of our society. The advent of the Internet in Malaysia dates back to 1995, which was considered the beginning of the Internet age in Malaysia. The aim of this paper is to trace the diffusion of Internet in Malaysia until present. The growth in the number of Internet hosts in Malaysia began around 1996. The country's first search engine and web portal company was also founded that year. From the first Malaysian Internet survey conducted from October to November 1995 by MIMOS and Beta Interactive Services, one out of every thousand Malaysians had access to the Internet then (20,000 Internet users out of a population of 20 million). The National Public Policy Workshop (NPPW) in 2005 proposed tremendous changes towards a strategy to move forward the uptake of ICT and internet in Malaysia. Among the outcomes of the NPPW is the High Speed Broadband initiative which was launched in 2010. As of July 2012 internet users in Malaysia reached 25.3 million. Out of that number, there are 5 million broadband users, 2.5 million wireless broadband users and 10 million 3G subscribers. With access to the Internet been largely achieved, the next step would be to maximise the use of the Internet in achieving digital inclusion and gaining cultural capital.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.243
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2013
Admission routes1
Has abstractyes

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