RETRACTED ARTICLE: Choral harmony: the role of collective singing in ritual, cultural identity and cognitive-affective synchronisation in the age of AI
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Purpose: This study examines how choral singing functions as a mechanism for sustaining ritual practice and reinforcing cultural identity. By integrating perspectives from musicology, social psychology, and cognitive science, it explores how collective vocal performance supports emotional attunement, group cohesion, and symbolic memory in culturally diverse contexts.Materials and Methods: A mixed-methods approach was applied, combining ethnographic observation, survey-based data, and cognitive measures with AI-informed frameworks such as voice emotion recognition and neural synchrony modeling. Case studies included Anglican cathedral choirs, Tibetan Buddhist chant groups, and Indigenous community choirs in Northern Canada.Results: Findings demonstrate that choral structures—such as harmonic entrainment and rhythmic alignment—foster affective convergence, enhance psychological resilience, and strengthen group synchrony. Participation in choir singing was also shown to increase self–other overlap and reinforce cultural continuity across traditions.Conclusions: The study affirms choral singing as both a symbolic and embodied practice of shared identity in the algorithmic era. It highlights the potential for AI-informed feedback systems to contribute to participatory music education and communal healing, providing a theoretical foundation and data reference for future interdisciplinary applications.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".