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Record W7046014609

Congregational Singing as Social Identity Shaping in Toronto Cantonese Worship Services

2023· article· en· W7046014609 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsSingingIdentity (music)WorshipFormative assessmentSocial identity theoryPerspective (graphical)
DOInot available

Abstract

fetched live from OpenAlex

Congregational singing is widely acknowledged as a doxology to God. Yet, opinions differ on how congregational singing influences the worshippers’ lives. Some believe that the group singing prepares the heart for the sermon, while others believe it has formational implications. This project explores the formative qualities in congregational singing, specifically emphasizing how such group activity informs the worshipper’s identity with God and how the worshipper relates to others through God. Biblical identities are utilized as the theological perspective to examine the identity messages expressed in congregational singing. The idea of identity hierarchy in social psychology, which includes social association and identity renegotiation, is used as the theoretical framework. This research strives to conduct critical inquiry and reflection on the theological, theoretical, and practical orientation of congregational singing in Greater Toronto Area Cantonese worship services. As such, a literature review concerning the theology, theory, and practice of congregational singing is discussed, from which implications are drawn to the research design process. Participant observation, online questionnaire, and qualitative interview methods are used to help extract and analyze how the social identity shaping process occurs in congregational singing within the research context.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.887

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.005
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.021
GPT teacher head0.265
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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