Bibliographic record
Abstract
Background Although there is a growing body of research on the nature and content of digital religion, we still know little about the prevalence of digital religious and spiritual practices among different populations in North America. To what extent do digital technologies play a complementary role to in-person religious and spiritual activities only, or do they also reach out to and provide important spaces for new segments of the population removed from more conventional forms of organized religion? Purpose The goal is to answer this research question and to explore the prevalence of different types of digital religion practices specifically among young adult Millennials in both the U.S. and Canada. Three contrasting hypotheses are tested: that digital religion practices are prevalent among large segments of the Millennial population and are part of a wider turn towards individual spiritualization (H1); that digital religion practices are another set of religiosity indicators showing signs of a secular transition among Millennials (H2); or that both trends are occurring in tandem, in that some Millennials are practising digital religion, mostly but not exclusively tied to in-person religious activities and socialization (H3). Methods To test these hypotheses, we generate a series of descriptive and logit regression statistical analyses using novel and high-quality 2019 Millennial Trends Survey data from both Canada and the U.S. Results We find that (1) digital religion as measured in this study is a phenomenon present among many Millennials, although it is also not present among all or a vast majority of this demographic; (2) this is especially the case for more passive forms of digital religion, notably digital content consumption, compared with more active forms such as social media posting; (3) social environment does play an important role, with digital religion practices much more prevalent in the generally more religious U.S. context, compared with the generally more secular Canadian context; and (4) digital religion practices are often, but not always, tied to other in-person religious and spiritual activities among Millennials. Conclusions and Implications We find support especially for our third hypothesis (H3) with these results. Consequently, we argue that we should understand the individual spiritualization and secular transition frameworks as complementary, rather than in complete opposition, regarding the prevalence of digital religion among Millennials.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".