Global health influences internationalization priorities at Canadian universities
Bibliographic record
Abstract
the web.In addition, other important health-related domain names including .doctor,.healthcare,.hospital,and .medicalare also pending award to exclusively private sector entities, the majority of which have no clear restrictions on use.Summary/Conclusion: The lack of adequate representation by the global public health community in applying and management of new health-related gTLDs is worrisome and could compromise the future quality of health information online.Countries, medical associations, civil society, and consumer advocates have objected to these applications on grounds that they do not meet the public interest and may not adequately engage in consumer protection activities.We argue that there is a crucial need for quality and evidence-based sources of health information online and that proper governance by the international community is necessary.This could be accomplished by requesting ICANN to re-categorize .healthas a sponsored gTLD and proactively appoint WHO its sponsor.By re-categorizing .health(similar to eligibility requirements in place since 2001 for .eduas a sponsored gTLD), WHO would develop policies to ensure accountability and transparency in gTLD operations that meet the best interests of the global health community and enforce eligibility rules regarding all future health registrants.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.003 |
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".