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Record W4412684271 · doi:10.46222/pharosjot.106.4015

Xenophobia and Love: Negotiating Reconciliation between South Africa and Nigeria in Light of 2 Corinthians 5:16-21 and 1 John 4:16-21

2025· article· en· W4412684271 on OpenAlexaff
John Arierhi Ottuh

Bibliographic record

VenuePharos Journal of Theology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican cultural and philosophical studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsXenophobiaNigeriansKinshipSociologyNegotiationCriticismImmigrationScholarshipBiblical studiesGender studiesTheologyAnthropologyPolitical scienceSocial scienceLawRacismPhilosophy

Abstract

fetched live from OpenAlex

Scholars of biblical and theological studies, have engaged extensively with the issue of xenophobia in South Africa, but less attention has been paid to its impact on Nigerian immigrants and diplomatic relationships from a biblical and theological standpoint. This paper re-examines xenophobia through the lenses of 2 Corinthians 5:16–21 and 1 John 4:16–21, proposing a theological framework for reconciliation between South Africans and Nigerians. It argues that the violent xenophobic attacks are rooted in hate/the absence of love, and posits love as a site for reconciliation and a rediscovery of authentic African kinship. The study highlights the impact of xenophobia on Nigerian immigrants— comparable to the experiences of Mozambicans and Zimbabweans—and explores how these wounds may be addressed through a theology of love. Utilizing an interdisciplinary approach, this paper draws on media reports documenting xenophobic incidents against Nigerians, alongside existing scholarly literature. The selected biblical texts are examined through the lens of African biblical criticism, specifically the evaluative approach. How may love theology help in the realisation of reconciliation and good kinship in 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.034
GPT teacher head0.299
Teacher spread0.265 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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