The humanities and health policy
Bibliographic record
Abstract
COVID-19 was a stark reminder that understanding a novel pathogen is essential but insufficient to protect us from disease. Biomedical and technical solutions are necessary, but they do not prevent or resolve misinformation, vaccine hesitancy, or resistance to public health measures, nor are they sufficient to advance the development of more equitable and effective healthcare systems. Responding to crises such as pandemics requires deep collaboration drawing on multiple methodologies and perspectives. Along with the science, it is imperative to understand cultures, values, languages, histories, and other determinants of human behaviour. This policy briefing argues that the humanities—a group of methodologically diverse fields, including interdisciplinary studies that overlap significantly with the social determinants of health—are an underused source of cultural and social insight that is increasingly important and could be better leveraged in such collaboration. Humanities disciplines approach health and illness as part of the human condition. Their historical perspective could be more effectively mobilized to explore the social and cultural context in which science exists and evolves, in turn, helping us understand the forces shaping perceptions, concerns, and assumptions.
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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".