COVID-19 pandemic: a time for collaboration and a unified global health front
Bibliographic record
Abstract
Coronavirus disease 2019 has, in the span of weeks, immobilized entire countries and mobilized leading institutions worldwide in a race towards treatments and preventions. Although several solutions such as telemedicine and online education platforms have been implemented to reduce human contact and further transmission, countries need to favour collectivism both within and beyond their borders. Inspired by experiences of previous outbreaks such as SARS in 2003 and Ebola in 2014-2015, global solidarity is a must in order to prevent further morbidity and mortality. Examples in leadership and collaborations ranging from research funds from the Bill and Melinda Gates Foundation to mask donations by the Jack Ma Foundation should be celebrated as examples to follow. Open communication and transparency will be crucial in monitoring the evolution of the disease in the global effort of flattening the curve. This crisis will challenge the integrity and fuel innovation of health systems worldwide, whilst posing a new quality chasm that warrants increased recognition.
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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 | 0.039 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.022 | 0.036 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".