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Record W4391666075 · doi:10.7202/1108942ar

Mises en présence du végétal et pratiques attentionnelles dans <i>Branché</i> et <i>We Move Together Or Not At All</i>

2023· article· fr· W4391666075 on OpenAlexaffvenueabout
Catherine Cyr

Bibliographic record

VenueTangence · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicDiverse Cultural and Historical Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Cet article porte sur l’expérience de réception de deux oeuvres performatives récentes s’attachant à mettre en relation des êtres humains et des végétaux : le spectacle ambulatoire Branché, des compagnies circassiennes Acting for Climate – Montréal et Barcode et l’installation chorégraphique de longue durée We Move Together Or Not At All de Sasha Kleinplatz. Par le biais d’une perspective soma-esthétique, qui fait du corps sentant et ressentant le site d’élaboration de la pensée, la réflexion examine la façon dont la rencontre avec le végétal et avec les autres composantes de l’environnement détermine et affecte l’expérience spectatorielle. Pour rendre compte de la dimension sensible de celle-ci, l’autrice privilégie, en partie, une méthodologie auto-ethnographique où des fragments tirés de son carnet d’observation, ressaisis par l’écriture, sont intégrés à l’analyse. Opérant un maillage théorique entre les champs des études en arts vivants, de l’écocritique, de la philosophie environnementale et des Critical Plant Studies, l’article place en son centre les effets et les affects induits par l’agentivité et la bioperformativité des végétaux en régime performatif. Il s’intéresse au déplacement et à l’intensification des pratiques attentionnelles et à la dimension éminemment politique de cette reconfiguration.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.017
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.064
GPT teacher head0.320
Teacher spread0.256 · 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
Published2023
Admission routes3
Has abstractyes

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