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Record W4392460619 · doi:10.4324/9781003335887-7

Little Visions and Grandiose Perceptions

2024· book-chapter· en· W4392460619 on OpenAlexaboutno aff
Priscilla Guy, Alanna Thain

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldComputer Science
TopicDigital Media and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsVisionPerceptionPsychologySociologyAnthropologyNeuroscience

Abstract

fetched live from OpenAlex

Manon Labrecque is a multidisciplinary artist based in Montreal, Canada. Her work is an encounter between the banal and the grandiose; the human body and the machine; the useless and the essential. A true polymath, Labrecque eludes categories and labels. A fan of handmade work, her relationship with technology unfolds through a lo-tech, autonomous aesthetic that engages the whole body. In her work – whether experimental video or multimedia installation – life and death continually collide in an eloquent fashion, evoking in turn the value of human life and the programmed obsolescence of the technologies that populate our world. From an aesthetic of the ordinary, she brings out layers of meaning underlying the tidiness of everyday life, notably through various techniques of disrupting and glitching the image. A mischievous artist, Labrecque is interested in an alternative beauty, that of failures and technical bugs. Her techniques of hacking and editing brings to the fore the fragility of video materials. In this way, the artist takes on the role of an image witch: her skilful technical hijackings reveal material-bodies and machine-bodies that bear the marks of fallible technology, while at the same time exuding an undeniable yet strange, even reinvented humanity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.332
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.015
GPT teacher head0.226
Teacher spread0.211 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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