Spinning the Web of Hate Online: A Critical Review from the Malaysian Laws
Bibliographic record
Abstract
<p>In a multi-cultural society, living in peace and tolerance are keys to development and sustainable economy. Undeniably, the efforts taken by all stakeholders are essential in materializing the future and dream of a peaceful country. Since its independence, Malaysia has been struggling to maintain the unity and integration of the three main ethnics, the Malays, the Chinese and the Indians. Matters pertaining to media especially publications of printed presses are strictly supervised by the Ministry of Home Affairs. However, with the inception of the Internet, regulating content of the Internet might be impossible for the law makers. This paper examines how the emergence of social networking website such as <em>Facebook, MySpace</em> and even <em>Tweeting</em> have been misused by irresponsible Internet users in Malaysia. Spinning the web of hate online is like spreading virus to the netizens and yet, its impact if it is not well tackled by members of society, it might spark serious problem to the unity and harmony of ethnics in Malaysia. Next, this paper examines how law responds to problems arose on the Internet. Finally, this paper suggests that supervision and monitoring content of the Internet which promote hate might be challenging but such problem need to be tackled by the authorities with extra vigilant and full coordination with all authorities.</p>
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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".