Introduction du générateur de liens sociaux par contextes (GLSC) dans une approche mixte : Etude sur l’hétérogénéité dans les liens de collaboration des chercheurs en biotechnologie et en sciences de la vie
Bibliographic record
Abstract
Introduction to the Social Context Name Generator (GLSC) in a Mixed Methods Approach — Study of the Heterogeneity of Researcher Cooperation Links in Biotechnology and Life Sciences: Are social dynamics in the field of biotechnology research more heterogeneous than those of other fields? To answer this question, we have constructed a tool producing comparable data on collaboration networks in three practice settings. The Contextual Social Ties Generator (CSTG) was administered in the fall of 2009 to 735 Quebec researchers from three sectors: health sciences, natural sciences, and engineering. Results indicate that the morphology of social networks significantly differs in the context of funding for a majority of researchers, regardless of their discipline. Results also counter the hypothesis according to which collaboration practices are significantly more heterogeneous in university biotechnology and life sciences 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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".