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Record W4296721730 · doi:10.25159/2522-6800/10290

Multilateralism and Management of Public Health Emergencies: A Case Study of the Africa Joint Continental Strategy for COVID-19 Outbreak

2022· article· en· W4296721730 on OpenAlexaff
Udoka Ndidiamaka Owie

Bibliographic record

VenueSouthern African Public Law · 2022
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsYork University
FundersCenters for Disease Control and PreventionAfrican Union
KeywordsMultilateralismPolitical sciencePandemicAgency (philosophy)OutbreakNationalismArticulation (sociology)Development economicsEconomic growthCoronavirus disease 2019 (COVID-19)Political economySociologyPoliticsEconomicsLawMedicineVirologySocial science

Abstract

fetched live from OpenAlex

As the world continues to grapple with a pandemic that struck in January 2020, the responses of governments and international organisations to control and combat it varied albeit with different levels of success. Some responses gave rise to nationalist as well as anti-multilateral and -international sentiments and actions, including the politicisation of the pandemic and a retreat from multilateral and international institutions of cooperation. However, the African Union’s response was a beacon of multilateralism as manifested by the adoption of an Africa Joint Continental Strategy for COVID-19 Outbreak. This instrument is a coherent and pervasive framework for combatting the pandemic and forms the basis of the continent’s response by informing the responses of the regional economic communities and member states. Despite this important outline of policy articulation that is geared towards informing policy convergence, the Africa Joint Continental Strategy remains under-analysed and under-appreciated as an example of agency, effectiveness, leadership, multilateralism and indeed sagacity.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.185
GPT teacher head0.367
Teacher spread0.181 · 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 designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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