The Model for a Path Forward: A Proposal for a Model Law Dealing with Cyber-Squatting and Other Abusive Domain Name Practices
Bibliographic record
Abstract
The internet has revolutionized the way we interact with information and each other. Among the internet’s many applications, e-commerce ranks at the top. Businesses derive significant value from a robust online presence which arguably begins with a strong domain name. Websites are identified by internet protocol (IP) addresses which consist of sets of numbers. The Domain Name System (DNS) is the internet’s address book. Its function is to allow internet users to identify websites with more memorable indicia than a set of numbers such as words, phrases and acronyms. Given that businesses often devote significant resources to growing brand recognition and the goodwill associated with their trademarks, many of them tend to register domain names under those trademarks. Domain names (unlike trademarks) are unique which further increases a trademark holder’s interest in securing ones that consumers would likely associate with its goods or services. Cyber-squatters seek to profit from the DNS by engaging in a form of “online speculation”. They register domain names that are either identical or confusingly similar to trademarks and then attempt to sell the domain name(s) to a legitimate trademark holder for a profit. The current regulatory framework dealing with cyber-squatting comprises of: 1) The Internet Corporation for Assigned Names and Numbers (ICANN) Uniform Dispute Resolution Policy (UDRP) and variants thereof; 2) The American Anticybersquatting Consumer Protection Act (ACPA); and 3) National trademark laws. This paper argues that while partially effective, the current framework is lacking. A review of UDRP panel statistics reveals a steady flow of complaints since 2000 with a marked upswing from 2005 forward. The WIPO Arbitration and Mediation Center, the largest UDRP resolution provider, receives between 1700-2600 complaints per year relating to cyber-squatting. Cyber-squatting is therefore clearly an issue that requires further or better regulation. The UDRP, ACPA and trademark statutes all suffer from significant shortcomings. This paper seeks to identify those shortcomings and propose a potential solution: a model law relating to cyber-squatting and other abusive domain name practices. The model law would create specific causes of action for cyber-squatting and the abusive practice known as “reverse-domain name hijacking”. It would also comport certain key provisions to aid in the harmonization of an internationally accepted body of “domain name law”. While a model law approach itself suffers from certain shortcomings (most notably the requirement that it be adopted in a significant number of states to become effective), this paper demonstrates that those shortcomings are far outweighed by its benefits.
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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".