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Record W2891034797 · doi:10.18432/ari29412

A Review of Two Conferences: The Head and the Heart of Arts in Prisons

2018· review· en· W2891034797 on OpenAlexvenueaboutno aff
Sarah Woodland

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2018
Typereview
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsGlobePrisonPoliticsSociologyMedia studiesVisual artsPolitical scienceLawCriminologyArtPsychology

Abstract

fetched live from OpenAlex

This is a comparative review of two conferences held in North America in March of 2018. Carceral Cultures was presented by the Canadian Association of Cultural Studies at Simon Fraser University, Vancouver, from March 1-4. The purpose of the conference was to bring together cultural theorists, practitioners and activists to contemplate the carceral. The Shakespeare in Prisons Conference was presented by the Shakespeare in Prisons Network at the Old Globe Theatre in San Diego, from March 22-25. The focus of this conference was to bring together artists and theorists who work in the field of arts in corrections, not limited to the works of the Bard. As a sometime practitioner-researcher of Prison Theatre I have found it interesting to compare the two conferences in terms of how each appealed to my head (cognition), and to my heart (affect), in engaging with the politics and aesthetics of arts in prisons. The conferences were divergent in so many ways, and yet now converge in my mind to deepen my understanding of the work that I do, and strengthen my resolve to continue resisting the broken (in)justice system through art-research-activism.

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)0.004
metaresearch head score (Gemma)0.018
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: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.370
GPT teacher head0.519
Teacher spread0.149 · 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
GenreReview

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
Published2018
Admission routes2
Has abstractyes

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