Exploring Gender Dynamics in Hajj Research: A Bibliometric Review
Bibliographic record
Abstract
Hajj is not only a religious ritual but also has social, economic, and political dimensions. Although, according to Sharia, the rituals of worship carried out by female pilgrims are the same as those of male pilgrims, from a gender perspective, the experiences, access, and challenges faced by female pilgrims are different from those of male pilgrims, including the physical-biological, cultural, and security threats, and even from the regulatory aspects of the hajj. This article, therefore, aims to analyze the gender dynamics that influence and are influenced by the hajj. The methodology used in this study is a qualitative approach complemented by descriptive statistical literature observations of 68 publications on the topic of hajj and gender. According to the findings, over 38 years, the number of documents on the hajj has been quite large (2320), but only 68 documents related to gender aspects. However, the discussion of gender aspects continues to increase, with a peak of nine publications in 2022 and seven publications in 2023. This increase indicates increasing recognition of gender dynamics in religious practices, the increasing global Muslim population, feminist and gender studies, accessibility of digital archives, and socio-political changes in various Muslim-majority countries. The largest number of publications came from Arabia, followed by the United States. Non-Muslim countries such as Australia, England, Canada, and Germany have made major contributions to integrating gender aspects into Islamic studies, including studies on the hajj. This study also exhibits that gender dynamics in Saudi Arabia after the launch of Saudi Vision 2030 have had a major impact on the implementation of a more inclusive and women-friendly hajj, especially related to the relaxation of provisions related to "mahram" and increased security to prevent sexual experiences that female pilgrims often experience.
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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 | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.037 | 0.046 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".