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Record W2562107504

The Making of the Iranian Refugee: From Revolution to Asylum

2015· article· en· W2562107504 on OpenAlexfundno aff
Alizee Zapparoli-Manzoni-Bodson

Bibliographic record

VenueTSpace · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
FundersFaculty of Education, Victoria University of WellingtonUniversity of Toronto
KeywordsRefugeePolitical scienceImmigrationHistoryLaw
DOInot available

Abstract

fetched live from OpenAlex

This paper follows the narratives and trajectories of Iranian asylum-seekers in Turkey as they undergo the process of applying for refugee status through the UNHCR. Ethnographic work conducted in three satellite cities, Yalova, Eskişehir, and Denizli, reveals the dynamic narratives of Iranian asylum-seekers as they grapple with their past and attempt to forge new identities while they navigate their way through difficult institutional frameworks. The findings of this study offer an overview of the different groupings of asylum-seekers and their narratives, including Azaris, political activists, members of the LGBT community, and members of religious groups including, Christians and Baha’i. Furthermore, this paper argues that the oppressive frameworks imposed on these individuals as they apply for asylum-seeker and subsequently refugee status create an environment that fosters the form of dynamism needed to fit within the parameters of the 1951 Refugee Convention’s criteria. Engaging the application of Ian Hacking’s notion of dynamic nominalism to the refugee category with the narratives of Iranians in Turkey offers insights into the performance of the refugee identity and how this process and the state of limbo experienced in Turkey impacts upon greater collective interpretations of the past, the homeland, the nation, ethnic identity, as well as present and future aspirations. Finally, parallels are drawn to narratives found in diasporic literary texts that further demonstrate the impacts of shifting identity.

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 score0.889
Threshold uncertainty score0.612

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 communication0.0000.000
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 head0.056
GPT teacher head0.358
Teacher spread0.302 · 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
Published2015
Admission routes1
Has abstractyes

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