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Open Source Biotechnology Platforms for Global Health and Development: Two Case Studies

2014· book-chapter· en· W2127910996 on OpenAlex
Hassan Masum, Karl Schroeder, Myra Khan, Abdallah S. Daar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe MIT Press eBooks · 2014
Typebook-chapter
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsUniversity of WaterlooOntario College of Art and DesignCentre for Global Health Research
Fundersnot available
KeywordsBiotechnologyOpen sourceBusinessBiologyComputer science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.343
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it