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Record W1980987499 · doi:10.3138/ctr.157.009

A Study in Dissonance: Performing Alternative Food Systems

2014· article· en· W1980987499 on OpenAlexvenueaboutno aff
Natalie Doonan

Bibliographic record

VenueCanadian Theatre Review · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsAttendanceCognitive dissonanceWhite (mutation)Citizen journalismVisual artsInstinctArtSociologyEcologyPsychologyPolitical scienceSocial psychologyBiology

Abstract

fetched live from OpenAlex

In December 2012, the Montreal performance art platform the SensoriuM presented Botanical Animal, featuring multimedia artist Amanda Marya White, whose work explores the relationships between people, cities and ecology. In Botanical Animal, White introduced a closed-loop system for growing tomatoes. The stage for this performance was a university staff kitchen, in which White displayed photographs, botanical drawings, and plants. The performance was participatory, as the artist instructed and invited those in attendance to make canned tomatoes and salsa together. In this paper, I analyze Botanical Animal to demonstrate the ability of performance art to extend affective capacities by reconsidering relationships between human, animal, organic and inorganic forms. Botanical Animal incited gut reactions in participants, thus increasing affective capacities and encouraging its publics to reimagine their places within globalized cycles of food distribution.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.020
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.020
GPT teacher head0.221
Teacher spread0.201 · 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".

Quick stats

Citations1
Published2014
Admission routes2
Has abstractyes

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