Spiritual and religious information experiences: An Annual Review of Information Science and Technology (ARIST) paper
Bibliographic record
Abstract
Abstract This chapter examines the contours of the religious and spiritual information experiences subfield through a review and content analysis of selected contributions from the past two decades in both information science and related fields. The research question that guides this review is: How have spirituality and religion been conceptualized in information science? Our focus has been on the LIS literature along with the fields of information behavior/practice/literacy, as well as related fields such as human–computer interaction (HCI), media and digital studies, religious studies (including sociology and anthropology of religion or religious tourism). Our aim was to highlight the ways in which the information science literature has contributed to advancing these conversations (using a collections/service/user experience or practice lens), but also how the discussions around the sacred, lived religion, contemplation, conversion or techno‐spiritual practices (to name a few) have provided insights into information phenomena and processes. We also discuss the evolution of, and practices associated with, social media and digital practices as well as a discussion of representation (or the lack thereof) of less mainstream religious and spiritual traditions in the literature reviewed. We end with suggestions for future research directions.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".