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Record W1737080355 · doi:10.21083/csieci.v8i2.2139

Sipping Water: Reverie and Improvisation

2013· article· en· W1737080355 on OpenAlexvenueno aff
Amanda Ravetz

Bibliographic record

VenueCritical Studies in Improvisation / Études critiques en improvisation · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsnot available
Fundersnot available
KeywordsImprovisationFeelingContext (archaeology)AmbivalenceIllusionAestheticsFocus (optics)Space (punctuation)PsychoanalysisArtPsychologyVisual artsSociologySocial psychologyPhilosophyHistoryCognitive psychology

Abstract

fetched live from OpenAlex

In 2011, Amanda Ravetz, Anne Douglas, and Kathleen Coussens worked with improvisation in the context of an experimental drawing process. Following workshops with artists Marina Abramović and Bronwyn Platten, the author wrote a score that involved sipping water slowly and drawing over an eight-week period. Drawing, performed for its own sake, produced a space of reverie. Interviews with participants and reflective notes made after work with the score suggest that beyond allowing us to focus on our bodily awareness, the experiment also brought to light ambivalent feelings about our prior training and existing skills. Interpreting these feelings as failure and loss, the paper draw links between reverie, illusion, and disillusionment and Gary Peters’ “ironic model” of improvisation (The Philosophy of Improvisation. Chicago: U of Chicago P, 2009. 94-6). However, where Peters avoids accounts of improvisation that focus on relationships between improvisors, the paper argues that reverie opens up a richly paradoxical space between improvisor and improvisor, as well as between improvisor and improvised, producing (shared) moments of rupture in which failure itself can signal new beginnings.

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)0.010
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0090.038
Scholarly communication0.0110.009
Open science0.0020.011
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.108
GPT teacher head0.384
Teacher spread0.276 · 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 designNot applicable
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

Citations2
Published2013
Admission routes1
Has abstractyes

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