Spiritual and Religious Information Practices: Lessons from the Field
Bibliographic record
Abstract
ABSTRACT This panel centers on presentations that address examples of spiritual and/or religious experiences through an information lens. The panelists will initiate a timely conversation about the ways in which individuals and communities make sense of their information worlds post pandemic and in contexts of high uncertainty (e.g., climate anxiety, increased polarization, AI developments, etc.). In examining coping strategies of the spiritual and/or religious kinds, the panel brings together scholars in a range of information fields to address several of the conference's themes, including new theoretical conceptualizations of the cultural, social, cognitive, affective, and situational aspects of information needs, searching, use, and sharing. As well, the session's examined contexts of everyday lived religion and spirituality can enrich our understanding of the intersections between health and well‐being, socio‐technical arrangements, and evolving and diverse information practices. The short presentations and interactive engagement bring together an international group of emerging and established scholars into conversation and will coalesce into the formulation of a research agenda on this topic. Speakers’ interventions will revolve around three connected questions: How can religious and spiritual experiences be theoretically contextualized within Information Science? What research approaches are most suitable for exploring spirituality/religion in our digital networked world? What ethical challenges do researchers encounter in undertaking this type of research (especially during a pandemic) and how can they be effectively addressed?
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".