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Record W4317929291 · doi:10.25159/1753-5387/11776

Wagar and Motley “Archaic” Vestiges: A Postmodernist Reading of Contemporary Somali Fiction

2022· article· en· W4317929291 on OpenAlexaff
Andrew Nyongesa

Bibliographic record

VenueJournal of Literary Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsSomaliIslamChristianityReading (process)LiteratureSociologyHistoryReligious studiesAestheticsAnthropologyPhilosophyArtLinguisticsArchaeology

Abstract

fetched live from OpenAlex

The advent of the modernist dream resulted in the universalisation of culture, which entails deliberate effort to abandon traditional ways of life that foster difference and instead embracing national cultures to bring different communities together. Colonialism in the Horn of Africa, for instance, brought different Cushitic communities under single political entities and most of them adopted Islam to find a common ground. Other communities in East Africa had to convert to Christianity to find a universal cultural bridge. This has resulted in the assumption that most African peoples are homogeneous given that past traditions that elevated difference have been eradicated by unifying factors such as modern states and conventional religions such as Islam and Christianity. A critical reading of some literary texts, however, demonstrates that such claims are partly unfounded because there exist aspects of pre-Islamic Somali religion along with the fundamental beliefs of Islam, which bolster difference. This article is a postmodernist reading of selected contemporary Somali fiction to investigate the influence of pre-Islamic Somali religion on contemporary Somali culture. Using the ideas of Jacques Derrida and Joseph Campbell, the study demonstrates the impact of myth and the ancient traditions on migration and contemporary culture in Nadifa Mohamed’s Black Mamba Boy and Nuruddin Farah’s Secrets.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.046
GPT teacher head0.321
Teacher spread0.275 · 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 designNot applicable
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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