Models of Cooperation and Knowledge Management: The Case of Biomedical Technology Management
Bibliographic record
Abstract
In the current biological paradigm, the biologist can no longer work in isolation. Networks of collaboration that are supported by information and communication technologies will enable researchers from a variety of disciplines and laboratories to generate and validate biological knowledge. Central to the development of medical tools and medical products is ensuring accessibility to knowledge for multiple researchers. Academia, government, and industry will all play a role in shaping policies that will enable cooperative knowledge production and the broad dissemination of biological knowledge. To better understand models of cooperation and knowledge management, we profile two case studies, the Agilent Microarrary Design program and the Accelrys Nanotechnology Consortium. Agilent has introduced the industry's first shared microarray design program. The program provides a new way of doing business with Agilent that allows scientists to share their custom microarray designs with designated groups while maintaining control of their intellectual property, or to share them with the scientific community at large. The Accelrys Nanotechnology Consortium provides a project framework that addresses the challenges of rational nanomaterials and nanodevice design. The Consortium gives members an edge in their R&D, increasing both its efficiency and effectiveness. It will further enhance the impact of software tools, contributing to R&D cost savings, supporting patent applications, facilitating interdisciplinary working, and supporting a smooth ongoing 'lab to fab' transition.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.013 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".