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Record W4387329228 · doi:10.1145/3610040

(Re)Capturing the Spirit of Ramadan: Techno-Religious Practices in the Time of COVID-19

2023· article· en· W4387329228 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMedia, Religion, Digital Communication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContemplationPandemicCoronavirus disease 2019 (COVID-19)ScholarshipAppropriation2019-20 coronavirus outbreakFace (sociological concept)SociologyIsolation (microbiology)Psychological resilienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AestheticsPolitical sciencePsychologySocial scienceSocial psychologyEpistemologyLawArtMedicinePhilosophy

Abstract

fetched live from OpenAlex

Ramadan is an important and blessed month for Muslims around the world. It is both a time of spiritual contemplation as well as an opportunity for reinvigorating communal bonds. The COVID-19 pandemic, however, disrupted many of the rituals and traditions of Ramadan. In this exploratory study, we present findings from 22 young Muslims' experiences with Ramadan and fasting during the pandemic. Our article sheds light on the techno-religious practices and information strategies used to mitigate isolation, share information, and celebrate Ramadan. We examine the sociotechnical configurations of religious rituals and highlight the resilience of these rituals even in the midst of a global pandemic. Our paper contributes to CSCW scholarship on technology appropriation and non-use as they relate to religious practices in the face of exogenous shocks such as the pandemic, and how design can better cater to the religious lives of individuals and communities.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.128
GPT teacher head0.353
Teacher spread0.225 · 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