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Record W2013856177 · doi:10.1186/1471-2458-7-346

How can developing countries harness biotechnology to improve health?

2007· article· en· W2013856177 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Public Health · 2007
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsCanadian Institutes of Health ResearchCentre for Global Health ResearchUniversity of TorontoUniversity Health Network
FundersInternational Development Research CentreOntario GenomicsOntario Genomics InstituteGenome Canada
KeywordsCommercializationDeveloping countryPublic healthCapacity buildingEquity (law)Public relationsBiotechnologyMedicineBusinessPolitical scienceEconomic growthMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: The benefits of genomics and biotechnology are concentrated primarily in the industrialized world, while their potential to combat neglected diseases in the developing world has been largely untapped. Without building developing world biotechnology capacity to address local health needs, this disparity will only intensify. To assess the potential of genomics to address health needs in the developing world, the McLaughlin-Rotman Centre for Global Health, along with local partners, organized five courses on Genomics and Public Health Policy in the developing world. The overall objective of the courses was to collectively explore how to best harness genomics to improve health in each region. This article presents and analyzes the recommendations from all five courses. DISCUSSION: In this paper we analyze recommendations from 232 developing world experts from 58 countries who sought to answer how best to harness biotechnology to improve health in their regions. We divide their recommendations into four categories: science; finance; ethics, society and culture; and politics. SUMMARY: The Courses' recommendations can be summarized across the four categories listed above: SCIENCE: - Collaborate through national, regional, and international networks- Survey and build capacity based on proven models through education, training, and needs assessments FINANCE: - Develop regulatory and intellectual property frameworks for commercialization of biotechnology- Enhance funding and affordability of biotechnology- Improve the academic-industry interface and the role of small and medium enterprise ETHICS, SOCIETY, CULTURE: - Develop public engagement strategies to inform and educate the public about developments in genomics and biotechnology- Develop capacity to address ethical, social and cultural issues- Improve accessibility and equity POLITICS: - Strengthen understanding, leadership and support at the political level for biotechnology- Develop policies outlining national biotechnology strategyThese recommendations provide guidance for all those interested in supporting science, technology, and innovation to improve health in the developing world. Applying these recommendations broadly across sectors and regions will empower developing countries themselves to harness the benefits of biotechnology and genomics for billions who have long been excluded.

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)0.002
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: Commentary · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.312
Teacher spread0.281 · 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