Bibliographic record
Abstract
A recent trend in public health campaigns has been to include non-human health data to capture all relevant variables related to human well-being. This specific approach is the foundation of the World Health Organization restructuring in the early 2000s as they adopted the “one health” framework. Politically, this movement is influential and draws significant health funding globally. "One health" is characterized by a multi-disciplinary collaboration between medical, veterinary, and health sciences. Similarly, the post-human turn in medical anthropology recognizes that viewing the non-human contributions to the cultural construction of health as symbolic does not adequately address how non-humans and nature independently contribute to human health realities. Ethnographic studies of the non-human perspective shed light on how humans are not the only beings that influence culturally constructed reality, nor are they exclusively in control of cultural phenomena. Theoretical trends in anthropology and public health seemingly converge; however, an artificial academic barrier between the sciences and social sciences remains. As these two disciplines are coming closer together through their data, breaking down structural barriers that prevent the successful integration of knowledge has potential to improve human health outcomes. Methodological concessions will have to occur on all sides to make the inclusion of the social sciences in public health possible. Doing so can bring academia closer to a comprehensive scientific understanding of human health.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".