Regulative measures for cybersquatting / Hershan @ Ray Herman, Lydia Salleh and Nur Farhana Said
Bibliographic record
Abstract
There had been a number of researches that investigated on the current situation of cybersquatting or domain name dispute in the world. This study was then conducted in order to investigate the effectiveness of current measure in combating crime of cybersquatting among trade markers in Malaysia. Specifically, this research aimed to highlight the effectiveness of the MYDRP in combating the issue of cybersquatting among trade marker. The primary focus of this research is the protection to the trade markers in online businesses. Weaknesses and strengths of MYDRP Rules and Policy are identified. To gather the needed data, a content analysis and interview were conducted. From the analysis and interviews, the current law, MYDRP, have loopholes and it is insufficient to provide protection to the trade marker's domain name. In conducting our research, we had made comparative studies on the law used to combat cybersquatting in USA, UK, India and Canada with the current measures in Malaysia to address this issue. From the comparisons, we examined why the laws that are used in addressing the domain name disputes in other countries are more effective than the one used in Malaysia. In the light of this problem the government of Malaysia should come out with a new regulative measure in combating cybersquatting as in the USA such as US Anti-Cybersquatting Consumer Protection Act 1999. This in turn benefits the court as it provides the court guidelines in solving the issue of cybersquatting. This also helps in reducing internet fraud as online business will be more protected and secured.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".