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Record W4367023732 · doi:10.3138/jh-57-3-2022-0014

Queering Sexual and Gender Citizenship in (Anarcho-)Feminist Zines in Post-Socialist Poland

2022· article· en· W4367023732 on OpenAlexvenueno aff
Barbara Dynda

Bibliographic record

VenueJournal of History · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsnot available
Fundersnot available
KeywordsGender studiesLesbianQueerCitizenshipSociologyGrassrootsMulticulturalismPolitical scienceLaw

Abstract

fetched live from OpenAlex

The article analyzes concepts of sexual and gender citizenship emerging in feminist, lesbian, and queer zines in post-socialist Poland. It focuses on oral history interviews and discourses manifested in the following zines: Łechtaczka (Clitoris), girlz get united! ( ggu!) and Lesbijki i Geje Międzynarodowa wymiana Przyjaźni ( Lesbian & Gay Hospitality Exchange International, L/GHEI). After contextualizing the Polish anarcho-feminist zine scene, this article analyses how post-socialist transnational discourses developed within underground publications. By focusing on ggu! and L/GHEI, it argues that feminist and queer zines reconceptualized sexual and gender citizenship while disrupting binary divisions (such as national/foreign, Eastern post-socialist/Western never-socialist geographies)—from both the anarchist and assimilationist perspectives. The article focuses then on a locally distributed zine: the lesbian-queer Łechtaczka. This publication shows how grassroots concepts of sexual and gender citizenship challenged those imposed by the Polish church–state system, as well as those harbored by misogynistic and homophobic groups of the punk scene. This article ends by highlighting how the analysis of ggu!, L/GHEI, and Łechtaczka allows us to see the complexity of identities and networks emerging in grassroots feminist communities and understand the complicated nature of discourses concerning the local and global reconceptualization of sexual and gender citizenship.

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)0.002
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 score0.864
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.065
GPT teacher head0.299
Teacher spread0.234 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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