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Record W1551605905 · doi:10.1111/nyas.12543

Convergent innovation for affordable nutrition, health, and health care: the global pulse roadmap

2014· article· en· W1551605905 on OpenAlexaff
Srivardhini K. Jha, John J. McDermott, Gordon Bacon, Chris Lannon, Poornima Joshi, Laurette Dubé

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsPulse (music)Health careBusinessEnvironmental healthMedicineEconomic growthComputer scienceTelecommunicationsEconomics

Abstract

fetched live from OpenAlex

The paper outlines how the principles of convergent innovation (CI) can be applied to bring about a transformation in the pulse value chain. The paper presents three pioneering CI initiatives--two in conception and one in operation--by various actors in the pulse ecosystem, which are delivering economic and human development impact in particular segments of the pulse value chain. It goes on to propose the way forward to scale up these efforts and connect them into a roadmap so as to achieve transformation throughout society, calling into action a number of actors in the ecosystem.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.011
Scholarly communication0.0100.016
Open science0.0010.013
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.095
GPT teacher head0.343
Teacher spread0.248 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2014
Admission routes1
Has abstractyes

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