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Record W2411188579 · doi:10.1177/1464700115604135

Newsletter networks in the feminist history and archives movement

2015· article· en· W2411188579 on OpenAlexaff
Cait McKinney

Bibliographic record

VenueFeminist Theory · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsFeminist movementLesbianSociologyKnowledge productionThe InternetFeminismFunction (biology)Media studiesGender studiesWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

This article examines how networks have been critical to the construction of feminist histories. The author examines the publication Matrices: A Lesbian/Feminist Research Newsletter (1977–1996), to argue that a feminist network mode can be traced through the examination of small-scale print newsletters that draw on the language and function of networks. Publications such as Matrices emerge into wide production and circulation in the 1970s alongside feminist community archives, and newsletters and archives work together as interconnected social movement technologies. Newsletters enabled activist-researchers writing feminist histories to share difficult-to-access information, resources, and primary sources via photocopying and other modes of print reproduction. Looking from the present, the author examines how network thinking has been a feature of feminist activism and knowledge production since before the Internet, suggesting that publications such as Matrices are part of a longer history of networked communications media in feminist contexts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.018
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0140.023
Scholarly communication0.0180.015
Open science0.0010.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.040
GPT teacher head0.273
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2015
Admission routes1
Has abstractyes

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