Bibliographic record
Abstract
Climate change is becoming a major public health problem given the long-term impact on mortality and morbidity through extreme weather, water scarcity, spreading of infectious diseases and the influence on vulnerable populations. The main sources of climate change are not generated in the same countries that are suffering the burden of health impacts. Coastal cities are more prone to flooding as a result of climate change, but these places might not be the top generators of CO2 emissions. Similarly, infectious diseases spreading north of the equator into new areas of habitat are more likely to affect the low socioeconomic populations in these areas who do not have access to appropriate prevention and health care. These require more thoughtfulness and consideration by population researchers, such as epidemiologists in attempting to identify cause-effect and focusing their research on identifying how actions by one population can impact another more vulnerable population. What are the ethical implications of such public health challenges and what kind of research should epidemiologists propose to address these challenges? These and other questions will be addressed as part of the discussion in this session.
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 | 0.171 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.062 | 0.074 |
| Insufficient payload (model declined to judge) | 0.014 | 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".