Bibliographic record
Abstract
This article examines music and music scholarship vis-à-vis research findings in addictions sciences. It explains how music is socially useful for preventing and treating addiction. Making music with others, and all of the social and cultural activities that go into doing so—musicking—can foster psychosocial integration and social cohesion, via specific cultural and musical mechanisms, and in ways that can salve addictions. Alexander’s social dislocation theory of addiction serves as the theoretical framework for the study. I draw empirical support for the discussion from my long-term ethnographic fieldwork on Indigenous addiction rehabilitation settings in Vancouver, Canada. My analysis of those settings finds that connecting socially via musicking in ways that can prevent and treat addiction happens through different ways of being, ideas and focuses of attention—such as constructs of ethnicity, around spirituality/religion, and social and political values—that are shared among musicking people and perceived via their eight senses (the auditory, visual, tactile, gustatory, olfactory, vestibular, proprioceptive, and interoceptive). This article responds to a lack of music and cultural research on the correlation between social disconnection and addiction as well as a lack of study on the social potential of musical cultures to prevent and treat addictions. The article lays groundwork for future research on the roles that musicking can play in addiction recovery.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".