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Record W2765762534 · doi:10.3138/ctr.172.017

Terms of Engagement: The Ethics and Aesthetics of Artistic Experimentation in the <i>Rencontres/Encounters</i> Project. A Dialogue of Sorts

2017· article· en· W2765762534 on OpenAlexvenueaboutno aff
Rachael Van Fossen, Lisa Ndejuru

Bibliographic record

VenueCanadian Theatre Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyAestheticsPerformative utteranceNegotiationContext (archaeology)RacializationSituatedValue (mathematics)Visual artsArtGender studiesSocial scienceRace (biology)Geography

Abstract

fetched live from OpenAlex

Situated within a context of social unease around xenophobic measures such as Québec’s Commission on Reasonable Accommodation and the infamous Charter of Québec Values, Rencontres/Encounters (R/E)’s research-creation process foregrounds what Ric Knowles terms “interculturalism” or “the contested, unsettling, and often unequal spaces between cultures, spaces that can function as performative sites of negotiation.” R/E blurs distinctions of what constitutes “community-engaged,” by experimenting with professional theatre artists from diverse or marginalized communities as an ethical, experimental approach to advancing social engagement processes and artistic creation. Can stepping into performance in the stories of others/another/an Other help build relationships and transform understandings across different life experiences, value systems, racialization, faiths, sexual and gender identities, etc.? The basis of the R/E experimental methodology in studio was to invite performance proposals centred on an ever-evolving set of questions. For example: What personal material can and should be used? How can risks be mitigated in future encounters? What are we looking for? How can we clarify the terms of engagement? Our team now is refining and adapting the experiments for dissemination in the form of workshops, and through a resource guide that will provide a menu of exercises, games, and performance proposals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.753
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.280
GPT teacher head0.486
Teacher spread0.206 · 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 teacher head, 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

Citations2
Published2017
Admission routes2
Has abstractyes

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