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Record W7039925195

Music museum curatorship: reclaiming rights and responsibilities for musicking on Minjerribah (North Stradbroke Island)

2016· other· en· W7039925195 on OpenAlexaboutno aff

Bibliographic record

VenueNOVA (University of Newcastle Australia) · 2016
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMusicalAgency (philosophy)Context (archaeology)Perspective (graphical)Action (physics)Process (computing)
DOInot available

Abstract

fetched live from OpenAlex

The Music Curator describes a staged approach to support the reclamation of rights and responsibilities for music-making on Minjerribah (North Stradbroke Island), in southern Moreton Bay, Queensland. This is part of enabling a localised movement toward claiming agency for planning the future course of musical development—especially early childhood musical development. The stages are to firstly understand the local music history from the perspective of local people. Secondly, the person-environment-occupation transactions are described to understand people’s musical relationships to Country. The Person-Environment- Occupation (P-E-O) Model, developed by Canadian occupational therapists (Mary Law and colleagues, 1996), is commonly used to assist individuals to adapt their occupational performance to adverse circumstances. Finally, local people and stakeholders are invited to engage in a strategic planning process to develop Music Action Plans for facilitating community musical development in the future. These stages of musical development are briefly discussed in this paper to highlight the role of the Music Curator, as an occupational therapist and ethnomusicologist, who facilitates the creative process. There are indications that the P-E-O model can be applied to the new context of music curatorship, especially for cultures with strong environmental relationships.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.160
GPT teacher head0.276
Teacher spread0.116 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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