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Record W3196332490 · doi:10.1007/s11013-021-09746-1

SymptomSpeak: Women’s Struggle for History and Health in Kosovo

2021· article· en· W3196332490 on OpenAlexfundno aff
Hanna Kienzler

Bibliographic record

VenueCulture Medicine and Psychiatry · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
FundersEconomic and Social Research CouncilCanadian Institutes of Health ResearchMcGill University
KeywordsPoliticsSpellContext (archaeology)SociologyPower (physics)Meaning (existential)Gender studiesPsychologySocial psychologyPolitical scienceLawHistoryPsychotherapist

Abstract

fetched live from OpenAlex

What are the linguistic dimensions of pain, and what kind of articulations arise from these painful experiences? How does the language of pain circulate, connect, and reach across histories, gendered realities, and social politics? In what ways might the language of pain act on and transform the world by shaping and changing socio-political agendas? I explored these questions among women in Kosovo and discovered a unique symptomatic language which I call SymptomSpeak. SymptomSpeak is a powerful language evoked, shared, and exchanged by women to articulate political, social, and economic grievances, to challenge societal norms, and to demand justice. The language itself consists of a detailed symptom vocabulary which is variously assembled into meaning complexes. Such assemblages shift depending on the social context in which they are conveyed and are referred to as nervoz (nervousness), mërzitna (worried, sad), mzysh (evil eye), and t'bone (spell). I describe in detail how women variously combine and exchange components of SymptomSpeak and, thereby, question dominant framings of reality. Thereby, my intention is to contribute to a new understanding of pain as language which straddles the fine line between socio-political commentary and illness; produces gendered political realities; and challenges the status quo through its communicative power.

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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.478
Threshold uncertainty score0.931

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 head0.037
GPT teacher head0.350
Teacher spread0.312 · 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
GenreCommentary

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

Citations1
Published2021
Admission routes1
Has abstractyes

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