Heritage Passions, Heritage Convictions, and the Rooted L2 Self: Music and Gaelic Language Learning in Cape Breton, Nova Scotia
Bibliographic record
Abstract
The present research examines the role of music and dance in motivating Gaelic language learning on Cape Breton Island (Canada). The Gaelic language, once thriving in this context, has declined in use but flourishes in both music and dance. This article presents the results of in‐depth interviews (60–90 minutes) with 10 accomplished adult musicians and dancers who described in rich detail connections between traditional music and the Gaelic language. The interview texts were analyzed using Leximancer 4.0, text‐mining software that performs an automatic analysis by deriving, in a grounded fashion, the key concepts in a text. Semantic and relational co‐occurrence information was extracted using artificial intelligence, producing a map of interrelations among concepts. Three interrelated concepts are introduced to describe community‐level motivational processes evident in the interviews: Rooted second language (L2) self, heritage passions, and heritage convictions. The rooted L2 self is defined by connections to place and speakers of the language; heritage passions reflect the development of emotional bonds, core values, and strengths; and heritage convictions capture deep‐seated beliefs, attitudes, and mindsets. Elements of both Gardner's notion of integrative motivation and Dörnyei's L2 self system are evident and are considered within Ushioda's (2009) person‐in‐context relational model, emphasizing the connections among learners and contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".