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Record W4280533974 · doi:10.3138/tric.43.1.f02

Theatrical Calls to Climate Action: Excerpts of a Conversation

2022· article· fr· W4280533974 on OpenAlexaffvenueabout
Sheila Christie, Beth Osnes, David W. Geary, Dennis Gupa, Ian Garrett, Jordan Hall, Katie Welch, Kendra Fanconi, Kimberly Skye Richards, Scott Sharplin

Bibliographic record

VenueTheatre Research in Canada · 2022
Typearticle
Languagefr
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of British ColumbiaYork UniversityUniversity of WinnipegCapilano UniversityCape Breton University
Fundersnot available
KeywordsHumanitiesPolitical scienceArtEthnologySociology

Abstract

fetched live from OpenAlex

Cet article est extrait d’une discussion sur la performance et l’action en faveur du climat qui a eu lieu à l’occasion du colloque annuel de l’Association canadienne de la recherche théâtrale (ACRT), Partition/Ensemble 2020. Dix artistes, activistes et universitaires rapportent leur expérience de la performance comme outil dans la lutte contre les changements climatiques. Leurs difficultés et leurs tactiques sont abordées, des exemples détaillés d’actions climatiques théâtrales sont exposés et la nécessité pour les autres artistes et activistes d’employer les outils du théâtre et de la performance pour raconter les récits de la crise climatique est soulignée de manière pressante. L’article, qui rend compte de la lame de fond d’énergie créative consacrée à la crise climatique, servira du même coup d’inspiration à celles et ceux qui veulent mettre leurs talents artistiques au service de la lutte contre les changements climatiques.

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)0.009
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: Other
Teacher disagreement score0.503
Threshold uncertainty score0.988

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0340.026
Scholarly communication0.0090.004
Open science0.0020.009
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0090.001

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 head0.205
GPT teacher head0.413
Teacher spread0.208 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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