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Sharing John Blacking

2018· reference-entry· en· W2966874103 on OpenAlexaff
Andrea Emberly, Jennifer C. Post

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsYork University
Fundersnot available
KeywordsRepatriationEthnomusicologyFieldnotesContext (archaeology)MusicalVisual artsHistoryScope (computer science)SociologyMedia studiesArtAnthropologyArchaeologyComputer scienceEthnography

Abstract

fetched live from OpenAlex

As ethnomusicological collections become accessible to individuals, communities, and institutions beyond the scope of the original collector, their contents are often repurposed, reimagined, and reinformed. With the growing engagement with repatriation by archives, individuals, and institutions, field recordings, fieldnotes, images, and other supporting materials offer tangible and intangible records of musical performance, context, and historical data to scholars and the communities that first offered their music for scholarly research. Drawing from the Vhavenda materials in the John Blacking collection housed at the University of Western Australia, this chapter uses two case studies, on children’s music and musical instruments, to explore some of the myriad issues surrounding the repatriation of a historical ethnomusicological collection. The goal is to help shape how future archivists, scholars, and communities engage with archiving and repatriating ethnomusicological collections.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.203
Threshold uncertainty score0.680

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0080.003
Scholarly communication0.0100.006
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2030.047

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 head0.224
GPT teacher head0.266
Teacher spread0.042 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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