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Record W4378194283 · doi:10.7202/1099880ar

The Studio in the Seminar: Performing Theory in an MFA Classroom

2023· article· en· W4378194283 on OpenAlexvenueno aff
Karin Shankar, Julia Steinmetz

Bibliographic record

VenuePerformance Matters · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusStudioScholarshipEmbodied cognitionClass (philosophy)SociologyField (mathematics)PedagogyMathematics educationPerformance studiesVisual artsEpistemologyPsychologyArt

Abstract

fetched live from OpenAlex

This article describes an "Introduction to Performance Theory" course that the authors co-teach to MFA students at Pratt Institute in Brooklyn, New York. Through the semester, we track genealogies of performance studies, highlighting the ways in which our interdiscipline has been incorporated as an academic field while still remaining sensationally unsettled in its interventions, methods, and objects of analysis. The focus of this article is on the ways we have tailored a performance theory course to serve MFA students—artists and makers across genre and discipline. The article offers our syllabus and ten practice-based assignments to illustrate how we encourage the artists in our class to engage with critical theory and performance studies scholarship in an embodied way. Bringing the studio into the seminar, our MFA students stage performance experiments related to each week’s readings. Our syllabus is accompanied by a reflection on co-teaching performance studies as a dynamic couple form that itself constitutes a performance of pedagogy, an enactment of sociality, and an embodiment of theory.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0110.009
Scholarly communication0.0100.005
Open science0.0030.011
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0210.009

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 head0.028
GPT teacher head0.252
Teacher spread0.224 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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