Musical citizenship as a means to disrupt exclusions: Potentials and limitations as understood in times of a pandemic
Bibliographic record
Abstract
This article focuses on the potential of in-group music lessons to foster musical citizenship. It further discusses the relation between musical citizenship and conventional citizenship and shows how musical citizenship reorientates our thoughts towards citizenship, particularly in the light of the recent pandemic. The discussion is based upon reflection on semi-structured interviews conducted during my ethnographic fieldwork research on musicking among refugees sheltering in reception centres. The discussion is framed with approaches to citizenship and musical citizenship. The discussion is structured in three parts. First, I conceptualize my interlocutors’ current ‘in limbo’ status. Second, I show how music learning in-group fosters musical citizenship and helps navigate exclusions. Third, the attention shifts on how music learning was impacted by the way that the lockdown was implemented as a measure to limit the spread of the pandemic, highlighting the inclusivity of ‘musical citizenship’ undermined by (conventional) citizenship and the relevant exclusionary policies.
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 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.052 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| 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".