Bibliographic record
Abstract
lobal health is a rapidly evolving field of biomedical science, but it also has a social, political and economic dimension that tends to capture people's imagination.This makes global health research amenable to community engagement and popularization among general public.However, it is difficult to know which strategies would work best to attract a large number of viewers to video materials that convey accurate global health information.This is particularly important in recent years, where it became apparent that any reliable information online could soon get its seductive, but inaccurate counterpart in an effort to enhance someone's personal "click bait" rate [1].Humans seem to be quite receptive to alternative views, especially when they are surprising, shocking or spectacular, or when they seem to be contrary to long-held popular beliefs or common knowledge [1,2].Such news always tends to be more interesting to broad population than the stories that merely consolidate or confirm the existing knowledge [1,2].But this also poses a potential danger when the creators of alternative and fake news "attack" an important public health issue and cause confusion among the population, thus risking reversal in many hard-achieved gains in population health [3].Anti-vaccine movements and are probably the most striking example of this risk [4].Increasingly, the scientists involved in global health, but also all other areas of science, are beginning to realise that moving forward and generating more knowledge for humanity is not our only task; in fact, it may no longer be our main task, either.Communicating the knowledge that has been generated to broad
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.018 |
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".