Synergies between the Developmental Origins of Health and Disease framework and multiple branches of evolutionary anthropology
Bibliographic record
Abstract
The Developmental Origins of Health and Disease (DOHaD) hypothesis derives from the epidemiological and basic/mechanistic health sciences. This well-supported hypothesis holds that environment during the earliest stages of life-pre-conception, pregnancy, infancy-shapes developmental trajectories and ultimately health outcomes across the lifespan. Evolutionary anthropologists from multiple subdisciplines are embracing synergies between the DOHaD framework and developmentalist approaches from evolutionary biology. Even wider dissemination and employment of DOHaD concepts will benefit evolutionary anthropological research. Insights from experimental DOHaD work will focus anthropologists' attention on biochemical/physiological mechanisms underpinning observed links between growth/health/behavioral outcomes and environmental contexts. Furthermore, the communication tools and wide public appeal of developmentalist health scientific research may facilitate the translation/application of evolutionary anthropological findings. Evolutionary Anthropology, in turn, can increase mainstream DOHaD research's use of evolutionary theory; holistic, longitudinal, and community-based perspectives; and engagement with populations whose environmental exposures differ from those most commonly studied in the health sciences.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".