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Record W4232999079 · doi:10.32920/ryerson.14667897

The Spatial Distribution of Development Across Africa and Its Underlying Sustainability Correlations

2021· preprint· en· W4232999079 on OpenAlexaff
Joshua Ali

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSustainabilitySustainable developmentDistribution (mathematics)Spatial analysisGeographyNatural resourceSocioeconomic statusSpatial distributionEnvironmental resource managementEnvironmental planningRegional sciencePolitical scienceEconomicsPopulationEcologySociology

Abstract

fetched live from OpenAlex

The realization of the critical issues that have been faced by the global community has put a particular focus on assessing the sustainable development of countries. Africa is an area that needs an assessment of sustainable development. With Africa holding over 52% of the world’s natural resources reserves, it is imperative to assess the sustainable development of the countries. The study evaluated what the underlying and spatial distribution on sustainable development was in Africa. Six dimensions of underlying sustainability and three significant signs of spatial autocorrelation were found. This provided information about the sustainability vulnerabilities within Africa. With the majority of the underlying dimensions displaying a socioeconomic focus on sustainability. Showing the collected indices result in a lack of coverage on the environmental side across the countries of Africa.

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)0.001
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.271
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 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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