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Record W3187097332

Weaponizing gender: the role of grammatical gender shifts in hate speech

2021· article· en· W3187097332 on OpenAlexaff
Magda Stroińska, Grażyna Drzazga

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPronounReferentGrammatical genderPsychologyLinguisticsNounCategorizationAnimacyPersonal pronounDemonstrativeCognitive psychologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

In languages that categorize nouns as belonging to distinct ‘grammatical genders’, moving an expression from one class to another implies changes to its status, and is often interpreted as derogatory. In Polish, shifting nouns with human referents from one category to another is usually perceived as having a “downward” orientation. It can therefore be used as one of the means of verbal aggression, especially name calling. Polish, with its gender sensitive morphology, has an array of gender shifting suffixes (cf. Swan, 2015; McConnellGinet, 2014). E.g. the word baba (feminine) is a crude though not vulgar reference to a woman – either one that is rude (e.g. okropna baba – ‘an awful woman’) or a peasant woman (baba ze wsi – ‘a country woman’). It can be shifted to babsko (neuter) or babsztyl (masculine) with progressively negative connotations. A compound baba-chłop (‘woman-man’, a masculine looking woman) is used for name calling towards transgender people. Transgender persons are also rudely referred to as ono (neuter) instead of on (masculine), ona (feminine), or a different preferred pronoun. A neuter pronoun used to refer to a person is not a neutral form of reference and is meant as a hurtful instrument of hate speech. A shift in animacy in order to diminish someone’s position involves the use of inanimate objects with lexicalized negative connotations to refer to people. As all Polish nouns are gendered, such nouns often agree in gender with the perceived sex of the referent, although this is not a rule. Thus, words such as szmata(‘rag’ fem. –derogatory, with reference to morality) are mostly used about women and burak (‘beetroot’ masc., implying lacking intelligence), mostly about men. If used across natural gender lines, the negative connotations become stronger (as in e.g. Szydło to jednak burak, ‘Szydło is indeed a beetroot’ used about former female Prime Minister Beata Szydło). In this paper, we analyse a corpus of Polish newspaper articles online and readers’ comments, as well as the Polish language corpora (unfortunately lagging significantly behind and not reflecting language changes). We analyse the frequency of the gender shifting suffixes and the nouns they attached to, looking at their collocations to identify semantic effects of gender shift. The results confirm the dramatic increase of verbal violence in Polish public discourse. Grammatical gender shift is not a new phenomenon but this seemingly innocent linguistic mechanism triggers immediate, even if unconscious negative associations, sometimes acting as dog whistles. As the current Polish government is engaged in a propaganda campaign against the LGBTQ community, the use of grammatical gender as a tool for hate speech and discrimination deserves some urgent attention. McConnell-Ginet, S. (2014). “Gender and its relation to sex: The myth of ‘natural’ gender.” In Corbett, G.G. (ed.) The Expression of Gender. Berlin/Boston: Walter de Gruyter. 3-38. Stroińska, M., G. Drzazga & K. Kurowska (2014). “Translating Grammatical Gender: Current Challenges.” Studia o Przekładzie. Warszawa. 175-190. Swan, O. (2015). “Polish gender, subgender, and quasi gender.” Journal of Slavic Linguistics. Vol. 23.1. 83-122.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.983

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 science0.0010.001
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 head0.299
Teacher spread0.262 · 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
Published2021
Admission routes1
Has abstractyes

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