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Record W4210680862 · doi:10.3138/ctr.189.008

Staff of Life: Preserving Yeast, Memory, and Humanity

2022· article· en· W4210680862 on OpenAlexvenueno aff
David Szanto

Bibliographic record

VenueCanadian Theatre Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAestheticsImprovisationImmediacyEnvironmental ethicsSociologyArtVisual artsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

The line between life and death is blurry. For one organism to live, others must die, turning first into food, then units of energy, and then fodder for new life. Performance itself is a play between liveliness and stabilizations, sensory immediacy and documented pasts. In the culture and practice of food, parallels abound. A notable yet humble example is witnessed in fermentation practice and the use of starter cultures, whether bacterial, yeasty, or both. Even as such cultures are consumed by a production process, they are renewed by it. This autoethnographic account of a cycle of three performances centred on an inherited sourdough starter explores the ways in which performance with food can act as a process of preservation, as well as an exploration of life, death, and states of being that may exist in between. In using a yeast culture that is more than 25 years old, I have attempted not only to perpetuate the tiny bodies within the flour-water paste but also to embody in a larger way the attitude and essence of the culture’s original user. Probing the preservation of humanness through food practice and artistic improvisation, I propose an alternate sense of memory, ecology, and mortality.

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)0.003
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0080.040
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0020.003
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 head0.036
GPT teacher head0.305
Teacher spread0.269 · 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
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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same venueCanadian Theatre ReviewSame topicGeographies of human-animal interactionsFrench-language works237,207