‘Our biology is listening’: biomarkers as molecular vestiges of early life and the production of positive childhood experiences in behavioral epigenetics
Bibliographic record
Abstract
Abstract The sciences of environmental epigenetics and the Developmental Origins of Health and Disease have become central in efforts to understand how early life experiences impact health across the life course. This paper draws on interviews with epigenetic scientists and laboratory observations in the United States and Canada to show how scientists conceptualize epigenetic biomarkers as molecular vestiges of early life and the consequences this has for postgenomic approaches to health, risk, and intervention. We argue that this process demarcates early life as the optimal time to study and intervene in health and positions biomarkers as conceptual and methodological tools that scientists mobilize to reimagine early life environments. These environments include Positive Childhood Experiences (PCEs), which reflect an emergent and increasingly prominent epistemic object in behavioral epigenetics. Though distinct from widespread research on Early Life Adversity, we show how PCEs continue to essentialize experience in gendered and individualized ways. Further, this paper suggests that focusing on biomarkers as molecular vestiges of early life allows scientists to create stability despite ongoing epistemological and biological unknowns in epigenetics and DOHaD. Our findings contribute new perspectives to social studies of epigenetics, biomarkers, and the production of novel epistemic objects in postgenomic knowledge practices.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".