How to do things with epigenetics: An investigation into the use of metaphors to promote alternative approaches to health and social science, and their implications for interdisciplinary collaboration
Bibliographic record
Abstract
Epigenetics is a multifaceted field within genetics and genomics which focuses on discovering mechanisms involved in gene expression and regulation. It came to public attention around the turn of the millennium when the human genome began to be deciphered. Initial findings from epigenetics research held the promise of changing how we think about health and illness, evolution and heredity; speculations about how individuals and populations could begin to control such processes through epigenetics were then picked up in the public realm. In this article we concentrate on two normally distant domains within the public sphere: the advertising of alternative health products and services, and the promotion of alternative approaches to social science, especially around how social science deals with the ‘biosocial’. Using insights from social representations theory and methods aligned with metaphor analysis, we investigate the meanings of epigenetics rooted in the use of metaphors and commonplaces that are circulating in current popular parlance and that are used to promote academic theories and ideas as well as tangible products and services. We compare and contrast them and assess their implications for collaborations between natural and social scientists. Our findings reveal some surprising similarities between the metaphors and commonplaces used by advertisers and social scientists, based in large part on the fact that both groups draw on the work of prominent epigeneticists. In both instances some fundamental tenets of mainstream biology are contested, and hopes are created for improving individual or population well-being through the manipulation of epigenetic mechanisms. Both domains share some misunderstandings of epigenetics that might lead to problems with interdisciplinary collaborations between social and natural scientists.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".