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Record W3117467891 · doi:10.1017/ytm.2020.7

Fostering Reconciliation through Collaborative Research in Unama’ki: Engaging Communities through Indigenous Methodologies and Research-Creation

2020· article· en· W3117467891 on OpenAlex
Marcia Ostashewski, Shaylene Johnson, Graham R. Marshall, Clifford Paul

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueYearbook for Traditional Music · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSociologyPolitical scienceEcology

Abstract

fetched live from OpenAlex

Abstract This article documents relationships, strategies, and activities involved in developing and carrying out collaborative community-engaged research for reconciliation, based on Indigenous methodologies and research-creation. It documents an example of Indigenous/non-Indigenous collaboration in Unama’ki (also known as Cape Breton, Canada), providing data towards the refinement of models of research designed to foster reconciliation, and contributing to a literature on Indigenous/non-Indigenous collaborations in ethnomusicology and related fields. While revealing some challenges in the process with respect to addressing local needs, it also describes transformations that can be achieved through effective collaboration, including ways in which universities can be involved.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.907
GPT teacher head0.447
Teacher spread0.460 · 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