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In Their Own Words: Disseminating Feminist Self-Art Histories in Sound Archives

2024· article· en· W4406619708 on OpenAlexaboutno aff
Federica Martini, Julie Enckell

Bibliographic record

Venuemagazén · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDigitizationObsolescenceNegotiationVisual artsPublic historySound (geography)Media studiesThe artsSociologyHistoryArtEngineeringSocial scienceAcousticsTelecommunications

Abstract

fetched live from OpenAlex

In 2009, artist Marysia Lewandowska began digitizing and sharing the Women Audio Archive (WAA) online. Begun in 1983 and conducted until the early 1990s, the WAA is a sound archive containing around 120 hours of public and private conversations recorded by the artist between London, the United States, and Canada with a Sony Walkman WM‑F1 cassette player. The WAA embodies the trajectory of feminist interview and oral history practices of the 1970s in an exemplary way, deliberately exploiting the potential of analog recording technology to capture traditionally marginalized voices of art and social history. Considering the obsolescence of recording technologies and dissemination channels, this paper interrogates the historical forms of accessibility to feminist art practices of self‑historicization and calls for reflection on the shift that the digitization of these sound documents entails. Particular attention will be given to the historical negotiations of intellectual co‑ownership and the contemporary contexts in which private analogue sound archives can become public and open source following their digitization.

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)0.004
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.023
Scholarly communication0.0100.011
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.046
GPT teacher head0.237
Teacher spread0.191 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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