Standardization and Omics Science: Technical and Social Dimensions Are Inseparable and Demand Symmetrical Study
Bibliographic record
Abstract
Standardization is critical to scientists and regulators to ensure the quality and interoperability of research processes, as well as the safety and efficacy of the attendant research products. This is perhaps most evident in the case of "omics science," which is enabled by a host of diverse high-throughput technologies such as genomics, proteomics, and metabolomics. But standards are of interest to (and shaped by) others far beyond the immediate realm of individual scientists, laboratories, scientific consortia, or governments that develop, apply, and regulate them. Indeed, scientific standards have consequences for the social, ethical, and legal environment in which innovative technologies are regulated, and thereby command the attention of policy makers and citizens. This article argues that standardization of omics science is both technical and social. A critical synthesis of the social science literature indicates that: (1) standardization requires a degree of flexibility to be practical at the level of scientific practice in disparate sites; (2) the manner in which standards are created, and by whom, will impact their perceived legitimacy and therefore their potential to be used; and (3) the process of standardization itself is important to establishing the legitimacy of an area of scientific research.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".