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Record W3010757235 · doi:10.3138/tric.40.1_2.153

“Festival Sites: The Civic and Collective Life of Curatorial Practice” An Interview with Deborah Pearson and Joyce Rosario

2019· article· en· W3010757235 on OpenAlexvenueaboutno aff
Keren Zaiontz

Bibliographic record

VenueTheatre Research in Canada · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsnot available
Fundersnot available
KeywordsConversationNarrativeThe artsMedia studiesArt historyVisual artsInterimSociologyManagementHistoryArtArchaeology

Abstract

fetched live from OpenAlex

What follows is an interview (over Skype) conducted by Keren Zaiontz in December 2017 with Deborah Pearson, founder of Forest Fringe, an artist collective she runs with Ira Brand and Andy Field, in the UK, and Joyce Rosario, Associate Artistic Director of the PuSh International Performing Arts Festival in Vancouver. At the time of this conversation, both Pearson and Rosario were in a series of remarkable professional transitions. Rosario, then Director of Programming, was at the helm of PuSh as interim Artistic Director. Norman Armour had stepped down from his position in April 2018, after fifteen years of managing the festival. Since the appointment, in 2019, of Franco Boni to Executive and Artistic Director, Rosario has moved into her current role as Associate. In 2017, Pearson and her Forest Fringe co-directors had rounded off ten years of curating groundbreaking work at the Edinburgh Festivals. They are currently producing an experimental narrative feature film, shot on location in the Channel Islands, as part of a company residency at ArtHouse Jersey.

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)0.008
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.079
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0390.026
Scholarly communication0.0130.012
Open science0.0020.012
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0080.001

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 head0.105
GPT teacher head0.342
Teacher spread0.237 · 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
Published2019
Admission routes2
Has abstractyes

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