Bibliographic record
Abstract
To maintain support for further investments into health research and prevent large groups of people from questioning modern science, researchers will increasingly need to master their communication of scientific progress in the 21st century to broad general population. The new generations, who inform and educate themselves online, prefer to make their own choices in what they view. This makes them vulnerable to many types of online misinformation, which is placed there mainly to attract their clicks. This evolving context could strongly undermine a consensus in the population over very important public health issues and gains. Therefore, we believe that it deserves to be recognised as a serious problem of our time that needs to be addressed. In addition to possible inaccuracies of the health information found online, the other component of the problem is how to make global public health topics and issues attractive for viewing online and engaging with. They need to compete with popular music, celebrity gossip, sports, movies and other forms of entertainment. In this issue, we bring a novel series aiming to explore effective strategies to promoting global health issues online and through other mass media, and reaching wide audiences.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.076 |
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".