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Record W4393988546 · doi:10.1177/00084298241238680

Creating a space for the sacred in a secular world: Shabbat dinners as a vehicle for collective effervescence, social connection, and meaning

2024· article· en· W4393988546 on OpenAlexvenueno aff
Shira Gabriel, Nicole Koefler, Veronica Schneider

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
FundersTempleton World Charity Foundation
KeywordsMeaning (existential)Connection (principal bundle)Space (punctuation)SociologyEpistemologyPhilosophyMathematicsGeometry

Abstract

fetched live from OpenAlex

Scholars have long argued that religious rituals are essential for maintaining society, bringing meaning to life and increasing connections via the collective effervescence that they engender. A world in which fewer people attend religious services does not mean that people no longer benefit from the important outcomes associated with religious rituals. The current research examined whether people experience collective effervescence and positive outcomes in home-based rituals—specifically, Shabbat dinners facilitated by a Jewish organization that provides resources for home-based Shabbat dinners. Survey data was collected on almost 1000 attendees of the Shabbat dinners. The results suggest that (a) Shabbat dinners include both components of collective effervescence (a sensation of sacredness and connection to others); (b) “transcendence” collective effervescence was related to religiosity and meaningfulness, whereas “connectedness”collective effervescence was related to belonging and satisfaction; and (c) using instructional resources predicted increased meaning via religiosity and increased satisfaction via belonging. The authors conclude that home-based rituals can provide valuable outcomes in a world in which people are less likely to attend religious services.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.337
Teacher spread0.286 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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