Open Source Biotechnology Platforms for Global Health and Development: Two Case Studies
Bibliographic record
Abstract
Using a case study approach, we examine the potential of open source biotechnology platforms for global health and development. Two initiatives relying on collaborative online platforms are analyzed: projects by the nonprofit institute Cambia and India’s Open Source Drug Discovery (OSDD) project. Cambia is addressing neglected diseases by making relevant patent information available through both its Patent Lens project and its Initiative for Open Innovation. OSDD complements this initiative through a collaborative platform and open source practices to accelerate drug development for neglected diseases. Cambia as well as OSDD, while sharing the goal of addressing basic needs of the developing world, have each implemented principles of the open source movement in different ways. We find that, in open source biotechnology for global health and development, at least three linked senses of “open” should be considered: open access, open licensing, and open collaborative platforms. We conclude that biotechnology for global health and development can move ahead through its own version of open source practices and collaborative online platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".