Bibliographic record
Abstract
Achievement of medical and public health goals requires mutual understanding between professionals and the public, a challenge in diverse societies. Despite their massive diversity humans belong to one species, with race and ethnicity used to subgroup/classify humans and manage diversity. Classifications are contextual and vary by time, place and classifier. As classifications show major variations in health status, and risk factors, research using race and ethnicity has accelerated. Medical sciences, including epidemiology, are learning fast to extract value from such data. Among the debatable issues is the value of the relative risk versus absolute risk approaches (the latter is gaining ground), and how to assess ethnicity and race (self-assignment is favoured in the UK and North America, country of birth in continental Europe). Racial and ethnic variations in disease and risk factors are often large and usually unexplained. There is a compelling case for ethnic monitoring, despite its difficulties, for tackling inequalities and as a foundation for research. Medical and public health goals require good data collected in a racism-free social environment. Health professionals need to find the benefits of exploring differences while avoiding social division. Advances in health care, public health and medical science will follow.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".