Organizing and experimenting: Genomic clinical trials as epistemic and organizational innovation
Bibliographic record
Abstract
In this paper, we focus on the study of six multi-center genomic trials, promoted by four different French cancer centers. Following recent work that investigated the organizational dimensions of biomedical innovations, we explore the “mutually constitutive relation between epistemic and organizational innovation” (Cambrosio, Keating & Nelson, 2014, p.13) in the domain of “precision medicine”. As a matter of fact, one may observe a great diversity of genomic trials regarding their scientific as well as their organizational design. We show that, in our case, the relations between organization/ing and experimentation/ing are manifold and, in fact, may be framed as cascading experimentations. First, the genomic clinical trials under investigation may be conceptualized as instances of experimenting (temporary) organization, since the organizational design that accompanies their deployment has changed over time in order to adapt to emerging problems. Second, these trials (are meant to) challenge and change the functioning of the healthcare organizations within which they are deployed. Reciprocally, the functioning of healthcare organizations enables and constrains the trials’ design as well as their successful completion. Fundamentally, these trials must be analyzed as epistemic as well as organizational innovation.
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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 | 0.066 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".