Functions, Influences & Effects of WhatsApp Use During the Movement Control Order (MCO) in Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
On March 18, 2020, the Malaysian government took a firm position to halt the spread of the COVID-19 pandemic by putting in effect the Movement Control Order (MCO). By that time, Malaysia had recorded deaths and the number of infections was hundreds. During this period, in addition to the use of popular social media platforms such as Facebook and Twitter for rapid information communication, the WhatsApp messaging app was also heavily relied upon during the MCO. In addition to providing information, WhatsApp was also considered to play an important role in daily tasks as well as in education. This article discusses the functions, influences and effects of the use of WhatsApp among Malaysians during the MCO. This research conducted a structured interview with 10 informants from diverse backgrounds and age range. The data was then transcribed verbatim. Analysis of the results revealed that WhatsApp's main functions were to facilitate communication with family members and employers, as well as the means for a rapid exchange of information. On the other hand, the informants revealed that some information shared in WhatsApp was unreliable since there were irresponsible people who were creating and sharing fake news. The informants were also aware that the dissemination of fake news will cause mass panic among the Malaysians. As such, the informants would refer to reliable sources to determine the authenticity of the news they have encountered. This action reflected a mature attitude using WhatsApp during the MCO.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it