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Record W2669091425 · doi:10.1080/14680777.2017.1326579

Affect bleeds in feminist networks: an “essay” in six parts

2017· article· en· W2669091425 on OpenAlexaboutno aff
Alexandra Juhasz

Bibliographic record

VenueFeminist Media Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)HonorPower (physics)SociologyGestureProcess (computing)AestheticsMedia studiesComputer scienceInternet privacyArtArtificial intelligenceCommunication

Abstract

fetched live from OpenAlex

This essay is one of many attempts to document and process a year-plus long feminist digital media project: ev-ent-anglement. The essay has an irregular construction in six sections to hold and honor the practices, concerns, and findings of the project that all aim to mark the power and violence left usually unregarded after the common and willy-nilly, usually corporate-abetted, movement of digital fragments of ourselves. The ev-ent-anglement, including this essay as one iteration, attempts to mark that every simple cut/paste in a digital environment has an unseen but sometimes felt consequence: a violence and a power. It asks: could this gesture have different meanings or purposes in other formats, environments, and communities? Is affect in Montreal similar to #affect in #Montreal? The essay suggests that perhaps with a dataset made with and for feminist social networks, with a dataset made to feel, our cut/pastes might maintain and pass on some of their original affect. That is to say, principled collections and ethical cuts within coherent datasets might allow for affect to both move and stay within feminist networks.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.090
GPT teacher head0.389
Teacher spread0.298 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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