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Record W2096077893 · doi:10.1377/hlthaff.27.4.1029

The Indian And Chinese Health Biotechnology Industries: Potential Champions Of Global Health?

2008· article· en· W2096077893 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.

Bibliographic record

VenueHealth Affairs · 2008
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsCentre for Global Health ResearchUniversity Health Network
Fundersnot available
KeywordsIncentiveBusinessChinaGlobal healthQuality (philosophy)Economic growthHealth careBiotechnologyMarketingPolitical scienceEconomicsMarket economy

Abstract

fetched live from OpenAlex

India and China have made major progress toward establishing research- and innovation-based health biotechnology sectors. Local health needs, including diseases that predominantly affect the poor, have driven much of this success. We argue that emerging domestic firms can play an important role as reliable and high-quality suppliers of existing products and as innovators for global health needs. Indeed, these firms' participation may make existing global health approaches more sustainable. However, global health stakeholders, including international donors and the Indian and Chinese governments, will need to fashion incentives for these companies to retain a strategic focus on the global poor.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.002
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 head0.010
GPT teacher head0.295
Teacher spread0.285 · 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