Bidirectional Shaping and Spaces of Convergence
Bibliographic record
Abstract
This article proposes a new bi-directional way of understanding the convergence of biology and computing. It argues for a reciprocal interaction in which biology and computing have shaped and are currently reshaping each other. In so doing, we qualify both the view of a natural marriage and of a digital shaping of biology, which are common in the literature written by scientists, STS, and communication scholars. The DNA database is at the center of this interaction. We argue that DNA databases are spaces of convergence for computing and biology that change in form, meaning, and function from the 1960s to the 2000s. The first part of the article shows how, in the 1980s, DNA sequencing shifted from passively incorporating computers to be increasingly modeled in digital coding and decoding. Information retrieval algorithms, reciprocally, were altered according to the peculiarities of DNA in the first sequence-storage databases. The second part of the article investigates the impact of these reciprocal interactions and globalization on the organization of research centers, ways of conducting big science, and scientific values. Through convergence and new technologies such as data mining, biology and computing were transformed technologically, institutionally, and culturally into a new bio-data enterprise called genomics.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".