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Record W3031681571 · doi:10.1017/9781108348447.009

The Australian Festival Network

2020· book-chapter· en· W3031681571 on OpenAlexaff
Sarah Thomasson

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsThe artsProject commissioningContext (archaeology)RepertoirePosition (finance)Relation (database)PublishingProduct (mathematics)National identityPublic relationsGlobal networkPolitical scienceMedia studiesAdvertisingSociologyGeographyEngineeringBusinessTelecommunicationsArtLawComputer science

Abstract

fetched live from OpenAlex

This chapter charts the Australian international arts festival network to demonstrate how it operates as part of a decentralised national theatre and to suggest how this local network operates as part of a broader global arts producer and market. By commissioning, producing, and disseminating new, distinctively Australian work of international standard, this network nationalizes the performing arts repertoire and creates a space in which to explore the ambiguities and cultural conflict inherent in nation-building projects. Rather than being insular and inward-looking, constructing national identity in the context of international arts festivals is necessarily conducted in relation to other countries and identities and is outward-facing to both reflect and position ‘brand Australia’ in the global marketplace. Part of the goal of this national network is to facilitate the transmission of Australian cultural product abroad, on the one hand, and to showcase productions from around the world that are similarly representing their local cultures, on the other.

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.165
Threshold uncertainty score0.553

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.001
Scholarly communication0.0060.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1650.034

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 head0.050
GPT teacher head0.247
Teacher spread0.198 · 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
GenreOther

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

Citations2
Published2020
Admission routes1
Has abstractyes

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