Music Heritage, Migration and Identity
Bibliographic record
Abstract
The panel draws on research that uses recorded songs, chosen by the research participants as their ‘Inheritance Tracks’, as a basis for collective listening and a prompt for the sharing of music-related reminiscences (Cohen, Grenier and Jennings, 2022; Cohen, Shaw and Waldock, 2022; Gardner and Hansen, 2024; Cohen, Shaw, Smith and Waldock, forthcoming 2025). This use of “Inheritance Tracks” as a methodological device was devised by media studies scholar Ros Jennings and inspired by a pre-recorded segment of the weekly BBC Radio 4 programme Saturday Live (2006). The segment features a celebrity guest invited to talk about and play excerpts from their two ‘inheritance tracks’: a piece of music—a song or “track” that they have inherited and one that they would like to pass on to others: (https://www.bbc.co.uk/programmes/p02p8zrg/episodes/downloads). Cohen, Shaw and Waldock used Inheritance Tracks as a prompt for remembering and storytelling when working with groups of older adults at community-based care facilities in the city of Liverpool. Through collaboration with Smith and Liverpool’s Yemeni, Ukrainian and Chilean communities, they subsequently adapted this method for research on music heritage, migration and identity. The main aims of this research, which began during the COVID pandemic, are to generate wellbeing benefits for communities from music-related reminiscence activities that directly strengthen inter-generational ties; enhance the visibility of the communities within the region; and deepen understanding of local cultural diversity and heritage. Running separately but in parallel to this was Gardner’s project ‘Mapping the Music of Migration’ (2019–2021, www.mamumi.eu), which likewise used ‘Inheritance Tracks’ as a methodological devise to investigate storytelling about music and its potential to enable intercultural exchange. The project involved partners from seven European countries and resulted in a collection of migrants’ ‘Song Stories’ – personal stories about music – that were made publicly available through an interactive app.
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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 | 0.003 | 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 | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".