The Human Genome Project as a Singular Episode in the History of Genomics
Bibliographic record
Abstract
In this paper, we progressively de-center the Human Genome Project (HGP) in the history of genomics and human genomics. We show that the HGP, understood as an international effort to make the human reference genome sequence publicly available, constitutes a specific model of genomics: prominent and influential but nevertheless distinct from others that preceded, existed alongside, and succeeded it. Our analysis of a comprehensive corpus of publications describing human DNA sequences submitted to public databases from 1985 to 2005 reveals a plethora of authoring institutions, with only a few contributing to the HGP. Examining these publications in a co-authorship network enables us to propose two different sequencing approaches—horizontal and vertical sequencing—whose changing dynamics shaped the history of human genomics. We argue that investigating the extent to which different institutions combined these approaches or prioritized one of them captures the history of genomics better than using the categories of large-scale sequence production and sequence use, as much scholarly literature concerning the HGP has done. Sequence production and use became fully distinct only within the HGP model, and especially during the last stages of this endeavor. By exploring a collaboration between Celera Genomics, a large-scale sequencing institution, and two medical genetics laboratories, we show the potential of our co-authorship network and its analysis for historical research. Our study connects the historiographies of medical genetics and human genomics and indicates that the so-called translational gap from sequence data to clinical outcomes may reflect the assumption that genomics was substantially different from prior and parallel genetics research. This essay is part of a special issue entitled The Sequences and the Sequencers: A New Approach to Investigating the Emergence of Yeast, Human, and Pig Genomics, edited by Miguel García-Sancho and James Lowe.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".