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Record W2918727515 · doi:10.5539/mas.v13n3p165

Homesickness and Displacement in Arab American Poetry

2019· article· en· W2918727515 on OpenAlexvenueno aff
Wafa Yousef Alkahtib

Bibliographic record

VenueModern Applied Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHomelandFeelingPoetryHistorySociologyArtPsychologyPolitical scienceLiteratureLawSocial psychology

Abstract

fetched live from OpenAlex

The aim of this study is to address the nostalgic elements found in the Writings of the Arab American poet Naomi Shihab Nye. Nye is an American Palestinian poet whose works are mainly concerned with revealing her father’s homesickness and detailing his lomging for his homeland and childhood memories. The study makes an attempt to prove that the overwhelming nostalgia bonds the person with his lost homeland, and prevents him from forgetting his past; therefore’ these feelings stand as a barrier between him and his new world. Displacement and homesickness are the main elements that increased the nostalgia of the immigrants for their homelands. To emphasize this, the current paper analysed some of Nye's poems which handle the sever nostalgia that Nye's father started suffering since the early beginning of his arrival to San Antonio, Texas in the United States of America. Besides, the study argues that the nostalgic feeling for the homeland has been transmitted from father to son/ daughter, although the later doesn't have any memories in his/ her ex- homeland. Thus, Nye herself started feeling the nostalgia for a past she has never lived and to a homeland she has never seen.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.275
Teacher spread0.263 · 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 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
Published2019
Admission routes1
Has abstractyes

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