A New Clinical Collective for French Cancer Genetics
Bibliographic record
Abstract
Collaborative forms of work such as extended networks, expert groups, and consortia increasingly structure biomedical activities. They are particularly prominent in the cancer field, where procedures such as multicenter clinical trials have been instrumental in establishing the specialty of oncology, and subfields such as cancer genetics, where bioclinical activities—for example, testing for breast and ovarian cancer (BRCA) genes and follow-up interventions—are predicated on the articulation of a number of tasks performed by new clinical collectives. In this article, we examine the founding and development of a French bioclinical collective—the Groupe Génétique et Cancer (GGC)—that coordinates and structures the activities of most French actors in cancer genetics and operates simultaneously in the clinical, research, and regulatory domains. To examine the group’s structure and dynamics, the article combines information gathered through traditional fieldwork methods with information elicited from a coauthorship and semantic-network analysis of the publications of GGC members from 1969 to 2001.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".