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Record W2803386160 · doi:10.5539/ells.v8n2p29

Arab-American Diaspora and the “Third Space”: A Study of Selected Poems by Sam Hamod

2018· article· en· W2803386160 on OpenAlexvenueno aff
Aseel Abdulateef Taha

Bibliographic record

VenueEnglish Language and Literature Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaAmericanizationBiculturalismImmigrationPoetryCultural assimilationGender studiesMulticulturalismSociologyDilemmaEthnic groupIdentity (music)RacializationCultural identityAnthropologyAestheticsPolitical scienceLiteratureNeuroscience of multilingualismSocial scienceRace (biology)LawArtPsychology

Abstract

fetched live from OpenAlex

Arab-Americans are an essential part of the multi-ethnic scene in the United States of America. They are increasingly making their voices louder. However, the process of Americanization has shaped Arab-American experience and literature both directly and indirectly. The early immigrants faced the pressures of assimilation into the American society, while also trying to preserve their Arab identity in the American-born generation. Cultural issues that are related to the immigrants’ experience, like biculturalism, bilingualism and dualism, are vitally depicted in Arab-American poetry. The American-born poets of Arab descent find in poetry a way through which they could express the dilemma of the Arab diaspora. Sam Hamod is one of the contemporary Lebanese-American literary figures whose works reflect the cultural conflicts from which the immigrants and their descendants suffer. Many of his poems deal with the concept of the “Third Space,” presented by the post-colonial theorist Homi K. Bhabha. It is a hybrid space in which the hyphenated individuals are stuck. In the multicultural and multiracial environment of the United States, the immigrants’ offspring occupy this in-between position where diverse cultures meet and clash in an endless process of identity splitting.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.009
GPT teacher head0.280
Teacher spread0.271 · 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
Published2018
Admission routes1
Has abstractyes

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