Drawing Religious Information Experiences Across Time: Timelines as a Graphic Elicitation Method
Bibliographic record
Abstract
Visual, arts-based methods are widespread in other social sciences but remain marginal in information science. Applying “timelining” (Sheridan, Chamberlain, and Dupuis, 2011) in information research can expand our understanding of connections among information, time, affect and inexpressible religious experiences, while fostering collaboration between researchers and participants and across disciplines.Les méthodes s’appuyant sur les arts visuels sont très répandues dans les autres sciences sociales, mais elles demeurent marginales dans les sciences de l'information. L'utilisation de la mise en séquence chronologique (Sheridan, Chamberlain, et Dupuis, 2011) dans les sciences de l'information est susceptible d’élargir notre compréhension des liens entre l’information, le temps, les affects et certaines expériences religieuses inexprimables, tout en favorisant la collaboration entre les chercheurs et les participants dans toutes les disciplines.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".