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Record W2342314624 · doi:10.1177/1464700115604136

Doing feminism in the network: Networked laughter and the ‘Binders Full of Women’ meme

2015· article· en· W2342314624 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFeminist Theory · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of CalgaryMcGill University
FundersResearch EnglandBrock UniversityConcordia UniversityMicrosoft Research
KeywordsFeminismAgency (philosophy)SociologyLaughterConstruct (python library)PoliticsCybercultureConsciousnessGender studiesMedia studiesThe InternetSocial sciencePsychologyComputer scienceSocial psychologyPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

We analyse how memes construct networks of feminist critique and response, mobilising the derisive laughter that energises current feminisms. Using the 2012 case of the ‘Binders Full of Women’ meme, we argue that feminist memes create online spaces of consciousness raising and community building. The timeliness, humorous affect and media techné of meme propagators become significant infrastructures for feminist critique, what we term ‘doing feminism in the network’. If the Internet is particularly good at facilitating the diffusion of feminist jokes, as others argue, we illustrate how the networking and distribution capacities of social media platforms such as Tumblr, Facebook and the online shopping site Amazon.com also cultivate new modes of feminist cultural critique and models of political agency for practising feminism through meme production and propagation.

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.

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)0.017
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: none
Teacher disagreement score0.582
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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