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Record W2924007381 · doi:10.1163/1573384x-20190109

A Look at the Yezidi Journey to Self-discovery and Ethnic Identity

2019· article· en· W2924007381 on OpenAlexaboutno aff
Peter Nicolaus, Serkan Yuce

Bibliographic record

VenueIran and the Caucasus · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupIdentity (music)PoliticsPower (physics)Political scienceFace (sociological concept)RefugeeIdentification (biology)SociologyGender studiesLawSocial scienceAesthetics

Abstract

fetched live from OpenAlex

Yezidi communities throughout the world are struggling with their collective identity; each at a varying and somewhat differing stage of self-discovery. While the present paper does seek to elaborate upon this journey for the Yezidis in Transcaucasia, Germany, Canada, and the USA, its main focus remains the analysis of the political developments in the Yezidi heartland of Northern Iraq. This is so that the reader may have a fuller picture of the catalysts spurring this Yezidi reimagining. On the one hand, you have the traditional Yezidi leadership caught within a complex series of client-patron relationships with Kurdish leaders: ethnic identification is leveraged for promises of influence and power. While, on the other hand, newly minted Yezidi military commanders, as well as grassroot figures and Yezidi NGOs, are trying to establish themselves as heads of a Yezidi community that is undeniably distinct from their Kurdish neighbours. This paper will further show that the withdrawal of the Kurdish Peshmerga in the face of the ISIS attack in 2014, the half-hearted responses of the regional Kurdish and Federal Iraqi governments, all coupled with the stalled return of Yezidi refugees contributed to a growing Yezidi movement to cement their identity, as well as satiate a growing urgency to define themselves as a distinct ethnoreligious entity.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.511

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 studies0.0010.000
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 head0.039
GPT teacher head0.331
Teacher spread0.291 · 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 designTheoretical or conceptual
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

Citations6
Published2019
Admission routes1
Has abstractyes

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