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Record W2965821207 · doi:10.5931/djim.v15i0.8982

Dutcher's Impact: Wolastioqiyik Lintuwakonawa as a Case Study for the role of Music in Preserving Traditional Knowledge

2019· article· en· W2965821207 on OpenAlexaffvenue
Kathryn E. Newhook

Bibliographic record

VenueDalhousie Journal of Interdisciplinary Management · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPersonal knowledge managementTraditional knowledgeKnowledge managementKnowledge transferDomain knowledgeKnowledge sharingOrganizational learningConversationKnowledge value chainKnowledge engineeringProcedural knowledgeExplicit knowledgeComputer scienceIndigenousSociologyCommunication

Abstract

fetched live from OpenAlex

Knowledge Management is a diverse field of study, dealing in the facilitation of knowledge sharing, the creation of knowledge systems, knowledge transfer, and knowledge preservation. Information professionals play an important role in helping these processes happen. Equally important is the preservation of Traditional Knowledge. Recognized as the knowledge Indigenous people have accrued over millennia, and formed through their interactions with their environment, Traditional Knowledge and its preservation also fall into the world of Knowledge Management. The performance of a piece of music is the manifestation of knowledge and, in the case of Jeremy Dutcher, is a form of knowledge preservation. Traditional Knowledge’s more fluid and dynamic nature is preserved in Dutcher’s 2018 album Wolastioqiyik Lintuwakonawa, where the artist creates a conversation between technical skill and the knowledge and language of the album. In the case of this paper, Dutcher’s album serves as an example of the way Traditional Knowledge can impact and provide new tools to the information profession and world of Knowledge Management.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.301
Teacher spread0.222 · 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 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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