How Do Postgenomic Innovations Emerge? Building Legitimacy by Proteomics Standards and Informing the Next-Generation Technology Policy
Bibliographic record
Abstract
How do postgenomic innovations emerge and become legitimate? Proteomics, a frequently utilized postgenomic technology, provides a valuable case study of the sociotechnical strategies used by an emergent scientific field to establish its legitimacy and assert political power. Chief among these strategies is standard making, an inherently political process that requires examination through a critical social science lens. We report in this study an original case study from interviews with proteomics scientists and observations at conferences of the Human Proteome Organization and Australasian Proteomics Society over a 5-year period (2011-2015). The study contributes new knowledge on how an emerging postgenomic science uses standard-setting practices to politically legitimize a hitherto contested technology. Drawing on legitimacy theory, we show how proteomics scientists and organizations used standards as strategic tools to establish the legitimacy of this postgenomic field and affirm that proteomics can generate verifiable and reproducible results, thereby establishing it as a legitimate scientific field. Notably, legitimacy can be leveraged, at the same time, to maximize political power vis-à-vis other fields of science and as such embodies power relationships. These data collectively inform the broader context, in which postgenomic innovations emerge and legitimize, both technically and politically, through standards making. These findings have relevance for the design of next generation technology policies by demonstrating that standards are not "just" standards or neutral constructs but also tools to leverage political power of and by science and innovation actors, as shown in this case study of the emerging early phase of proteomics from 2011 to 2015.
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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 | 0.060 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".