Bibliographic record
Abstract
Completing a pilgrimage has often been touted as a transformational experience. Yet, pilgrimage as an information context is sorely lacking in our field, despite the valuable insights it could provide into the complex information environments and evolving states of those who undertake pilgrimage. In this article, I examine a specific pilgrimage: the Hajj in Mecca (Saudi Arabia). Preparing for Hajj involves a series of stages encompassing material, spiritual, and informational dimensions. Using a qualitative and exploratory approach, this study applies the lens ofpilgrimage as ‘lived religion’ and makes explicit the detailed activities and outcomes of pilgriminformation practices, and the ways in which information in its multiple forms (textual, spiritual,corporeal, etc.) has mediated and shaped the pilgrims’ journey. I build on established theoriesin information behavior and meaning-making in the context of everyday life, as well as theliterature on pilgrimage and pilgrimage as ‘lived religion’ to relate the participants’ encounterwith Hajj and their experiences toward becoming a Hajji/-a (someone who has completed theHajj). Findings based on interviews with twelve (12) global Hajj goers suggest that pilgrims’information practices are varied, and transcend both individual (cognitive, affective) as well as social processes (through shared imaginaries and a translocal network of people and resources). The study illustrates the importance of examining diverse transformational experiences in LIS, and the rich contributions that our field can make to these research 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".