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Record W4200069222 · doi:10.24073/jga/2/02/04

Globalization and Public Health in Rural Zones: Lessons from Sub-Saharan Africa

2021· article· en· W4200069222 on OpenAlexaff
Benjamin Poku, Jean-Leopold Kabambi

Bibliographic record

VenueJournal of global awareness · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGlobalizationPublic healthRural areaEconomic growthBusinessDevelopment economicsNatural resourceRural healthSocioeconomicsGeographyHealth carePolitical scienceEconomicsMedicine

Abstract

fetched live from OpenAlex

Distant rural regions of Sub-Saharan Africa are often coveted by foreign investing companies for their natural resources. However, the rural populations do not always take advantage of the economic benefits resulting from those investing activities. These increasing activities do not leave without harming the health of rural communities as they rely on community-based traditional and ancestral practices such as fishing and hunting, traditional medicine, spiritual ceremonies, among others, to survive. We aimed to analyze selected indicators of public health in rural zones highly impacted by globalization factors using existing database and literature research. Given the complexity of the situation, efforts and strategies to mitigate the negative effect of globalization on the health of rural communities must include not only urgent and binding commitment of all stakeholders but also a multi-sectorial long-term approach to increase the health of rural Sub-Saharan African populations while taking advantages of local know-how.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.057
GPT teacher head0.332
Teacher spread0.276 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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