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Record W3117507922 · doi:10.20355/jcie29373

On Understanding Syrian Diasporic Identities through a Selection of Syrian Literary Works

2020· article· en· W3117507922 on OpenAlexaffvenueabout
Ghada Alatrash, Najat Abed Alsamad

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsMount Royal University
Fundersnot available
KeywordsDiasporaNarrativeSpanish Civil WarIdentity (music)HistoryDislocationLiteratureSociologyGender studiesArtAestheticsArchaeology

Abstract

fetched live from OpenAlex

As of late August 2018, a total of 58,600 Syrian refugees have arrived in Canada (Government of Canada, 2019). The Syrian Diaspora today is a complex topic that speaks to issues of dislocation, displacement, loss, exile, identity, a desire for belonging, and resilience. The aim of this paper is to offer a better understanding of the Syrian peoples who have become, within the past four years, part of our Canadian citizenry, local communities, and members of our schools and workforce. By engaging the voices of Syrians through their literary works, this essay seeks to challenge some of the ontological and epistemological underpinnings that have historically defined Syrians and to offer alternate ways in which we may better know and understand what it means to be Syrian today. Historically Syrians have written and spoken about exile in their literature, long before the the Syrian war began in March of 2011. To deliver a sense of Syrian identities, a selected number of pre-Syrian-war writers and poets are engaged in this essay, including Nizar Kabbani, Muhammad al-Maghut, Zakaria Tamer, Mamduh Adwan, Adonis and Nasib Arida; furthermore, to capture a glimpse of a post-war sentiment, the voice of Syrian novelist Najat Abdul Samad, whose work was written from within the national borders of a war-torn Syria, is brought into the discussion.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.359

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.080
GPT teacher head0.358
Teacher spread0.278 · 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

Citations2
Published2020
Admission routes3
Has abstractyes

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