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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 OpenAlexaff
Nadia Caidi, Cansu Ekmekcioglu, Rojin Jamali, Priyank Chandra

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.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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

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

Citations12
Published2023
Admission routes1
Has abstractyes

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