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Record W2156231812 · doi:10.21083/csieci.v8i2.2143

Improvising Freedom in Prison

2013· article· en· W2156231812 on OpenAlexvenueno aff
Nayanee Basu

Bibliographic record

VenueCritical Studies in Improvisation / Études critiques en improvisation · 2013
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsnot available
Fundersnot available
KeywordsImprovisationRealmDancePrisonSociologyEthnographyBENGALSubject (documents)CriminologyAestheticsGender studiesLawPolitical scienceHistoryVisual artsArtAnthropology

Abstract

fetched live from OpenAlex

Basu, as ethnographer, details the experiences of two individuals, Alokananda Roy, established classical dancer and Mr B.D. Sharma, appointed Additional Director General of Correctional Services, West Bengal Police in 2005. Basu’s fieldwork (which begins in 2008) describes and analyses their introduction of dance into the male and female prison communities of West Bengal. Small workshops importantly become major public performances of Tagorean dance dramas played to large and influential public audiences. Improvisation in this project works at a number of levels. The subject and content of the dance dramas, while built around classical themes of good and evil, the rule bound v unpredictability, lend themselves to formal artistic improvisatory interpretations. These are not just performed, but lived in the experiences of prisoners and guards as well as through civic processes as these slowly bend in response to the new energy. The effort gradually provokes change, seen simultaneously in the attitudes and practices of the prisoners towards greater levels of interest, self reliance, respect, and self organisation, as well as in the policy and rules of penal organisation in the region of West Bengal. Artistic improvisation opens up doubt in this case study in the social realm, challenging the instituted physical and emotional separation between prisoners and non-prisoners in quite practical, judged ways that do not dissolve but alter the system.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.419
Teacher spread0.337 · 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.

Study designObservational
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

Citations1
Published2013
Admission routes1
Has abstractyes

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