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Record W2034123119 · doi:10.3138/ctr.158.003

“Well, Melody, what is your skirt gonna turn into now?”: An Interview with Melody Mangler

2014· article· en· W2034123119 on OpenAlexvenueaboutno aff
Ines Ortner

Bibliographic record

VenueCanadian Theatre Review · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsBurlesqueArtVisual artsConnotationIngenuityPerforming artsLiteratureAestheticsLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Abstract: Melody Mangler is a highly acclaimed burlesque performer, teacher, costumer, and writer from Vancouver. In 2009, the Burlesque Hall of Fame recognized her talents with the “Best Debut” Award for her outstanding performance piece Venus in Spring. As a founding member of the Screaming Chicken Theatrical Society and their current artistic director, she contributes significantly to the Vancouver burlesque scene. This interview centres on the role costumes play in Melody Mangler’s performance process from their inspiration to their construction, and as a stage partner. The conversation covers various aspects of costume design: the historical burlesque, the connotation of fashion-before-function, the ingenuity required during the building process, and the technical complexity in the creation and play all burlesque costumes involve. Mangler also speaks to the creative adjustments of conventions, the inherited satirical characteristic of the neo-burlesque as well as the significance of sex-positivity and body-positivity.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.583

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0180.010
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.035
GPT teacher head0.247
Teacher spread0.212 · 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 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

Citations0
Published2014
Admission routes2
Has abstractyes

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