Health Domains for Sale: The Need for Global Health Internet Governance
Bibliographic record
Abstract
A debate on Internet governance for health, or “eHealth governance”, is emerging with the impending award of a new dot-health (.health) generic top-level domain name (gTLD) along with a host of other health-related domains. This development is critical as it will shape the future of the health Internet, allowing largely unrestricted use of .health second-level domain names by future registrants, raising concerns about the potential for privacy, use and marketing of health-related information, credibility of online health content, and potential for Internet fraud and abuse. Yet, prospective .health gTLD applicants do not provide adequate safeguards for use of .health or related domains and have few or no ties to the global health community. If approved, one of these for-profit corporate applicants would effectively control the future of the .health address on the Internet with arguably no active oversight from important international public health stakeholders. This would represent a lost opportunity for the public health, medical, and broader health community in establishing a trusted, transparent and reliable source for health on the Internet. Countries, medical associations, civil society, and consumer advocates have objected to these applications on grounds that they do not meet the public interest. We argue that there is an immediate need for action to postpone awarding of the .health gTLD and other health-related gTLDs to address these concerns and ensure the appropriate development of sound eHealth governance rules, principles, and use. This would support the crucial need of ensuring access to quality and evidence-based sources of health information online, as well as establishing a safe and reliable space on the Internet for health. We believe, if properly governed, .health and other domains could represent such a promise in the future.
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 | 0.051 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.030 | 0.045 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".