Webcast Video Editorials: The Launch of eJIAS - The Electronic Journal of the International AIDS Society
Bibliographic record
Abstract
It is not a frequent event for an editor to be able to announce a brand new journal. But we can this month. In partnership with the International AIDS Society (IAS) and with funding from Bristol-Myers Squibb, Medscape General Medicine now contains the brand-new eJIAS as another specialized eSection with its own Editor Chief, Dr. Mark Wainberg[1] of Montreal's McGill University, and a distinguished editorial board. As you, the reader, knows, HIV/AIDS continues to be a serious epidemic; [it's] in control, meaning steady-state, some developed countries, but out of control many developing countries, poised now Asia to potentially dwarf the devastating scope of the epidemic many African countries. You ask, why another AIDS journal? Aren't there enough already? There are indeed many excellent journals that publish research and clinical observations about HIV. But what has been wrong is the imbalance between those countries that have the worst HIV problems and those countries that publish most of the articles. It is a developing- vs developed-world dysynchrony. This new journal, eJIAS, is free of charge to readers and to authors all countries; being electronic, it will be fast; and it will emphasize publications from workers developing countries. This time, for a change, the have-nots win. Good show! That's my opinion. I'm Dr. George Lundberg, Editor of MedGenMed.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.392 | 0.181 |
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".