Connected pilgrims: A case study of the Motawif booking system on X
Bibliographic record
Abstract
Abstract Hajj is the spiritual and religious journey of a lifetime for many Muslims around the world. In June 2022, the Saudi Ministry of Hajj and Umrah decided to introduce a new travel booking system that mandated pilgrims from Europe, America, Australia, and New Zealand to reserve directly their pilgrimage travels, thus bypassing the local Hajj travel operators. Additionally, Motawif was deployed just a few weeks before the 2022 Hajj. This announcement triggered a wave of panic among would‐be pilgrims and resulted in an intense social media conversation. This article examines the conversation that took place on Twitter (now X) for the period following the launch of Motawif and up until the completion of the 2022 Hajj. Using computational social science methods, we undertook several analyses of the Twitterspace to understand the perceptions of the would‐be pilgrims with the Motawif system. The study illuminates how forms of community organizing in online spaces (and associated information practices and discursive strategies) have contributed to reconstituting the Hajj pilgrim's identity and agency in ways rarely seen before among this community. Our findings also point to various strategies and information practices of opposition that enable connective action through solidarity and recognition of shared grievances.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".