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Record W7135718362

Covid-19 and Perceptions of Digital Religious Options on the Island of Ireland – Promoting Faith, or Hastening Secularization?

2025· article· en· W7135718362 on OpenAlexaboutno aff
Gladys; id_orcid 0000-0001-9894-033X Ganiel

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMedia, Religion, Digital Communication
Canadian institutionsnot available
Fundersnot available
KeywordsIrishEnthusiasmProtestantismPerceptionFocus groupThe Internet
DOInot available

Abstract

fetched live from OpenAlex

Religious groups’ relationships with digital technology changed during the Covid-19 pandemic. This paper draws on data gathered as part of a three-year, multi-context research project, including online questionnaires of leaders and members, interviews, and analysis of hundreds of documents published by religious groups and faith-based outlets. It charts the original enthusiastic uptake of digital technology among Christian groups on the island of Ireland, which led some leaders to advocate cultivating digital religious cultures as a counter to secularization. It also explores how Christian enthusiasm for digital religious options waned, especially within Catholicism, in the latter stages of the pandemic. Catholic leaders observed declines in in-person attendance, provoking discourses linking digitalization to secularization. Protestant groups remained more optimistic, encouraging members to use digital resources to supplement in-person practice. While noting these trends, we profile the enthusiastic cultivation of a digital religious culture among participants in an online compline (night prayer) group in a Catholic parish in Belfast. Finally, we contrast Irish Catholic approaches to digital religious options with Catholic approaches to the digital in the other contexts included in our research study: Canada, Germany, and Poland, noting that despite misgivings, the Irish Catholic Church was more positive about maintaining digital options than in these other contexts.

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)0.000
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 score0.694
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.035
GPT teacher head0.295
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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