Covid-19 and Perceptions of Digital Religious Options on the Island of Ireland – Promoting Faith, or Hastening Secularization?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".