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Record W2989656411 · doi:10.1162/dram_a_00879

Theatres of Control: The Performance of Algorithms and the Question of Governance

2019· article· en· W2989656411 on OpenAlexaff
Ulf Otto

Bibliographic record

VenueTDR/The Drama Review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCybernetics and Technology in Society
Canadian institutionsCanadian Association for Theatre Research
Fundersnot available
KeywordsTurbo PascalCorporate governanceConstitutionComputer scienceSortingPascal (unit)Context (archaeology)Control (management)AlgorithmArtificial intelligencePolitical scienceLawManagementEconomicsHistoryProgramming language

Abstract

fetched live from OpenAlex

Algorithms must be understood in regard to the governance of populations by means of numbers. Turbo Pascal's Algorithmen, a recent performance that experiments with sorting its audience, raises the question of governance and technology in the context of performance. Aesthetic innovation and individual experience are no longer at stake for performance within digital cultures, but rather the constitution and governance of collectivities.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.029
Scholarly communication0.0120.012
Open science0.0020.003
Research integrity0.0030.004
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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.

Study designTheoretical or conceptual
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

Citations5
Published2019
Admission routes1
Has abstractyes

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