Global infectious disease research collaborations in crises: building capacity and inclusivity through cooperation
Bibliographic record
Abstract
BACKGROUND: The initial research requirements in pandemics are predictable. But how is it possible to study a disease that is so quickly spreading and to rapidly use that research to inform control and treatment? MAIN BODY: In our view, a dilemma with such wide-reaching impact mandates multi-disciplinary collaborations on a global scale. International research collaboration is the only means to rapidly address these fundamental questions and potentially change the paradigm of data sharing for the benefit of patients throughout the world. International research collaboration presents significant benefits but also barriers that need to be surmounted, especially in low- and middle-income countries. CONCLUSION: Facilitating international cooperation, by building capacity in established collaborative platforms and in low- and middle-income countries, is imperative to efficiently answering the priority clinical research questions that can change the trajectory of a pandemic.
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 | 0.040 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.042 | 0.049 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".