What Will Happen to Biomedical Research in Low-and-Middle Income Countries in the PostCOVID-19 World?
Bibliographic record
Abstract
Scientific research is essential for a nation's development and is vital for generating solutions to population's health. Individual country's capacities to prevent and respond to public health issues, including health crises, is built with long-term investment in highly qualified professionals, infrastructure, and uninterrupted operating funding. Most Latin American countries, especially those at the bottom of the human development list, have limited capacity even though they are hot spots for tropical and other emerging infectious diseases. This weakness deepens these countries' dependence on nations with higher development and corresponding scientific capacity. The current COVID-19 pandemic has wreaked havoc on the health of the world's population and the global economy. Countries that lagged behind prior to the pandemic now face a myriad of additional challenges. On a more optimistic note, the pandemic could serve as a wake-up call for governments and funding agencies to strengthen scientific capacity around the world, so that we are better prepared to address the public health issues caused by current and prevalent diseases and by future diseases of pandemic potential.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".