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Record W3201608154 · doi:10.1177/00219096211039539

India’s Health Diplomacy as a Soft Power Tool towards Africa: Humanitarian and Geopolitical Analysis

2021· article· en· W3201608154 on OpenAlexaff
Rajani Mol, Bawa Singh, Vijay Kumar Chattu, Jaspal Kaur, Balinder Singh

Bibliographic record

VenueJournal of Asian and African Studies · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSoft powerDiplomacyGeopoliticsForeign policyHumanitarian crisisPolitical scienceInternational relationsLeverage (statistics)Hard powerGlobal healthEconomic growthHealth careDevelopment economicsChinaPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

India and Africa have been sharing a multidimensional relationship of cooperation and friendship since the ancient civilizations. The COVID-19 pandemic has brought new possibilities and opportunities for India to leverage its soft power diplomacy towards Africa. The paper’s main focus is to analyze how India has made soft power part of its foreign policy and examine India’s relationship with the African continent through health diplomacy. A literature search was done in major databases, such as Web of Science, Medicine/PubMed, Scopus, OVID, and Google Scholar search engine to gather relevant information. Through humanitarian assistance and geopolitical influence, India had won the support and heart of Africans. Besides, India has become a global healthcare provider in the African continent through its global health diplomacy and vaccine diplomacy. India has achieved impressive gains through its soft power diplomacy and has become a compassionate and benevolent actor in the African continent.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.346
Teacher spread0.318 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations42
Published2021
Admission routes1
Has abstractyes

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