Bibliographic record
Abstract
This paper describes evidence that led to the concept of biological embedding and research approaches designed to elucidates its mechanisms. Biological embedding occurs when experience gets under the skin and alters human biological and developmental processes; when systematic differences in experience in different social environments in society lead to systematically different biological and developmental states; when these differences are stable and long term; and, finally, when they have the capacity to influence health, well-being, learning, or behavior over the life course. Biological embedding emerged from insights in population health on the unique characteristics of socioeconomic gradients: Ubiquity in poor and postscarcity societies alike; gradient seen regardless of whether socioeconomic status is measured by income, education, or occupation; cutting widely across health, well-being, learning, and behavior outcomes; replicating itself on new conditions entering society; and, often, showing that flatter gradients mean better overall societal outcomes. Most important, the gradient begins the life course as a gradient in developmental health, suggesting that the emergence of a multifaceted resilience/vulnerability early in life is the best place to look for evidence of biological embedding. To understand its character, the metaphor of the "archeology of biological embedding" has been used, wherein the surficial stratum of the "dig" is experience and behavior, the shallow stratum is organ system and cellular function, and the deep stratum is gene function. We are now ready to address the fundamental question of biological embedding: How do early childhood environments work together with genetic variation and epigenetic regulation to generate gradients in health and human development across the life course?
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".