MétaCan
Menu
Back to cohort
Record W2023879359 · doi:10.1126/science.1062633

Harnessing Genomics and Biotechnology to Improve Global Health Equity

2001· letter· en· W2023879359 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueScience · 2001
Typeletter
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsToronto Public Health
Fundersnot available
KeywordsEquity (law)Action planGenomicsGlobal healthDeveloping countryBusinessBiotechnologyEconomic growthHealth equityPolitical scienceGenomeHealth careBiologyEconomicsGeneticsManagementGene

Abstract

fetched live from OpenAlex

With decisive and timely action, genome-related biotechnology can be harnessed to improve global health equity. In June 2002 in Kananaskis, Canada, leaders of the G8 industrial nations will develop an action plan to support implementation of the New African Initiative. By extending their discussion of health issues raised in the New African Initiative to include genomics, G8 leaders could signal their intention to increase global health equity by preventing a health genomics divide from developing. There are already some early and growing examples of genome-related biotechnology being applied successfully to health problems in developing countries. But how can genomics be systematically harnessed to benefit health in developing countries? We propose a five-point strategy, including research, capacity strengthening, consensus building, public engagement, and an investment fund.

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 categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.601
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0060.005
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 head0.019
GPT teacher head0.334
Teacher spread0.315 · 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