Everything Old Is New Again: Does the '.sucks' gTLD Change the Regulatory Paradigm in North America?
Bibliographic record
Abstract
In 2012, the Internet Corporation for Assigned Names and Numbers (“ICANN”) took the unprecedented step of opening up the generic Top Level Domain (“gTLD”) space for entities who wanted to run registries for any new alphanumeric string “to the right of the dot” in a domain name. After a number of years of vetting applications, the first round of new gTLDs was released in 2013, and those gTLDs began to come online shortly thereafter. One of the more contentious of these gTLDs was “.sucks” which came online in 2015. The original application for the “.sucks” registry was somewhat contentious with a number of countries and others opposing the application. Nevertheless, ICANN granted the rights to a Canadian company, Vox Populi, which has subsequently made a splash in the domain name market offering a variety of pricing levels for different “.sucks” domain names. Complaints have been made to Industry Canada about the activities of Vox Populi in the domain name space, but, so far, the Canadian government has bowed out of involvement in the issue. This Article explores the way that the new gTLDs in general, and the “.sucks” domain name in particular, have affected the landscape for domain name regulation with a particular focus on North America.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".