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Record W3157676900

The Politics of Gender in Azeri-Russophone Literature

2020· article· en· W3157676900 on OpenAlexaffabout
Leyla Seyidova

Bibliographic record

VenueStudent Research Proceedings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsMacEwan University
Fundersnot available
KeywordsModernityPoliticsColonialismArmenianGender studiesHegemonyEthnic groupSociologyPolitical scienceHistoryAnthropologyLawAncient history
DOInot available

Abstract

fetched live from OpenAlex

Sevinc Jafar’s novel Fakhriya (2018), first written in Russian and then translated by Javid Abbasov to Azeri, focuses on one Azeri woman’s experience during the Karabakh war (late 1980s-1994). The Karabakh war was an ethnic and territorial conflict in the enclave of Nagorno-Karabakh in southwestern Azerbaijan, between the majority ethnic Armenians of Nagorno-Karabakh (backed by Armenia) and the Republic of Azerbaijan. During the Karabakh war, Armenian troops demonstrated horrific brutality, especially towards women, whom they brutally and sometimes publicly sexually assaulted (Isgandarova 176). In my thesis, I will argue that despite the cultural hegemony under Russian colonialism, women in Azerbaijan used the discourse of colonial modernity to transcend traditional gender roles. I will explore the ways in which colonial language politics implicitly inform constructions of gender in Soviet and independent Azerbaijan; indeed, the fact that Jafar’s work was written in Russian suggests that it remains the language of modernity in Azerbaijan. As I will show, Jafar uses Russian to express women’s experiences of sexual violence during the Karabakh war beyond the gender roles that dictate the limits of appropriate speech in Azeri. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Sara Grewal Department: English

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.165
GPT teacher head0.467
Teacher spread0.301 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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Same venueStudent Research ProceedingsSame topicTurkey's Politics and SocietyFrench-language works237,207