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Record W4220726771 · doi:10.5406/21567417.66.1.09

Motivating Traditional Musicians to Learn a Heritage Language in Gaelic Nova Scotia

2022· article· en· W4220726771 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEthnomusicology · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsCape Breton University
Fundersnot available
KeywordsNova scotiaFluencyHeritage languageIsolation (microbiology)Cultural heritageMusicalSociologyLinguisticsPsychologyHistoryVisual artsPedagogyArtMathematics educationEthnologyArchaeology

Abstract

fetched live from OpenAlex

Abstract It is urgent that we learn how to motivate learners of threatened heritage languages. Motivational theories, however, are weakened when they consider heritage languages in isolation from the rest of the culture in which they are enmeshed. By drawing on psycholinguist Zoltán Dörnyei's L2 Motivational Self System to analyze interviews with ten traditional musicians from Nova Scotia with varying degrees of Gaelic fluency, we find that musical knowledge inspires and enriches their language learning and vice versa. It is the interviewees’ holistic understanding of Gaelic culture, as well as the culture's links to community and heritage, that motivates them.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.974

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0270.000

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 head0.085
GPT teacher head0.275
Teacher spread0.189 · 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