On the institutional and intellectual division of labor in epigenetics research: A scientometric analysis
Bibliographic record
Abstract
While numerous qualitative social scientific analyses of (environmental) epigenetics have been published, we still lack a macro-level, quantitative assessment of the field of epigenetics as a whole. This article is aimed at filling this gap. Mobilizing an extended version of the Web of Science, we constituted a corpus of 199,484 documents (articles, reviews, editorial material, etc.) published between 1991 and 2017 and performed several scientometric analyses to map out the development and structure of the epigenetics field. Three main results were drawn from these investigations. First, contradicting the hope expressed by some social scientists that their disciplines will find solace in epigenetics’ social biology, it is striking that the scientists, journals and institutions that drive most of the research in the field are overall little concerned with social and environmental dimensions of gene expression. Second, and confirming existing qualitative analyses, we find that epigenetics is constituted by diverse networks of scholars, institutions and research specialties that enjoy relative autonomy from each other and approach epigenetics through different thematic interests, from cognitive functions to cancer, to DNA methylation in plants and molecular biology. Third, findings obtained from the bibliographic coupling showed that these different networks became more and more autonomous over the last decade, which suggests that we are currently witnessing the constitution of a scientific archipelago akin to that of behavior genetics (Panofsky, 2014: 33) rather than to a discipline per se. At the same time, this differentiation was less pronounced conceptually speaking, as we also observed a clear standardization of the keywords used in epigenetics articles between 1991 and 2017, with DNA methylation and RNAs serving as rallying signs for different communities of researchers.
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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 | 0.090 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.117 | 0.237 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| 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".