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Record W4313681502 · doi:10.5206/tba.v4i1.14885

hard ground / long road

2023· article· en· W4313681502 on OpenAlexaffvenueabout
Maria Kouznetsova

Bibliographic record

Venuetba Journal of Art Media and Visual Culture · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsMagnetic tapeTape recorderVisual artsComposition (language)Sound (geography)Noise (video)NarrativeComputer scienceArtEngineeringAcousticsElectrical engineeringArtificial intelligenceLiterature

Abstract

fetched live from OpenAlex

My work is anchored in analog technology, found materials, field recordings, and the search for constellations of micro-narratives. In August 2021, I drove from San Francisco, California to London, Ontario. During the seventeen days on the road, I recorded radio noise (AM, FM picket-fencing) as well as my voice describing the environment surrounding the road at the times when I could not stop to take a photo. The sounds collected from the long road are recorded chronologically in an intaglio print. These recordings are the foundation of multidisciplinary sound and visual projects that coalesce as an installation exploring place/no-placeness, transience, precognition, repetition, delays, disappearance, and noise.
 Works selected for tba journal are pinhole photographs, a double-sided intaglio print, and a sound composition, that are all a part of the series described in my artist statement and share the title hard ground / long road. The print is reproduced at its original scale; the text is followed by the reverse side of the print, which is a line drawing. Two pinhole photographs bookend the works on paper. The sound companion to the visual works is a composition created on and with magnetic tape. The composition foregrounds incidental sounds of the process of making the work: tape splicing and rewinding. recorder fixing, road noise and tape hiss, woven with cut-up radio recordings.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.041
GPT teacher head0.372
Teacher spread0.331 · 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 designNot applicable
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
Published2023
Admission routes3
Has abstractyes

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Same venuetba Journal of Art Media and Visual CultureSame topicGeographies of human-animal interactionsFrench-language works237,207