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Record W3036154426 · doi:10.3968/11631

Arab American Novel: Development and Issues

2020· article· en· W3036154426 on OpenAlexvenueno aff
Abdalwahid Abbas Noman

Bibliographic record

VenueStudies in literature and language · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOppressionInjusticeGender studiesRacismIdentity (music)Stereotype (UML)SociologyFace (sociological concept)ImmigrationRefugeePolitical scienceHistoryAestheticsSocial scienceLawPsychology

Abstract

fetched live from OpenAlex

The novel in the Arab-American literature is absolutely considered a modern writing in its existence and styles except The Book of Khalid (1911) which was written in the first decade of the twentieth century. It becomes the main genre in the body of Arab-American literature and the large numbers of Arab-American novels have been published as a result of the considerable efforts made after the eighties of 20th century. Arab-American novels generally tackle issues such as the problem of identity, anti-Arab racism, marginalization, immigration and situations of refugees, nostalgia, and exile. They also foreground social problems Arab-American communities face as heterogeneity, generational differences, oppression, stereotype, social injustice and anti-assimilationist discourses. Moreover, the Arab-American novels try to create reconciliation between the Western and oriented cultures and reconciliation between the culture and values of the West and the East, the displacement of Palestinians, war and poverty in the Middle East, and so forth. This paper aims at providing an overview of the development of Arab-American novel as well as exploring the most common issues and themes discussed by this kind of genre in the Arab-American literature. It also tries to investigate in detail the concept of identity as a debatable issue in the contemporary Arab-American literature.

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)0.000
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 score0.359
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.038
GPT teacher head0.349
Teacher spread0.311 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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