Mapping of Religious Moderation Literature in Higher Education: A Bibliometric Review
Bibliographic record
Abstract
Religious moderation has become a critical focus in higher education, particularly in fostering social harmony and inclusive national identity. This study aims to analyze current trends in religious moderation research within the higher education sector and identify opportunities for future research development. A Systematic Literature Review (SLR) approach was utilized, guided by PRISMA protocols to ensure accuracy and relevance. A total of 160 publications were initially gathered from ERIC, Taylor Francis, and Mendeley databases indexed in Scopus. Based on defined inclusion criteria, 23 publications from 2019 to 2024 were selected for in-depth analysis. Bibliometric analysis was conducted using VOSviewer 1.6.20 software. Findings reveal fluctuating trends in religious moderation research, with a notable increase in publications during 2022, likely influenced by contemporary social issues and educational policies. Indonesia emerged as the leading contributor, followed by Jordan and Canada. Predominantly, qualitative and quantitative methodologies were employed. Thematically, research concentrated on radicalism, curriculum development, and religious tolerance. Density mapping further indicates a research gap in the exploration of nationalism within the context of religious moderation. The limited focus on nationalism suggests an opportunity for future research to bridge this gap and reinforce the role of religious moderation in cultivating inclusive national identity within higher education. This study underscores the need for continued and diversified research on religious moderation, emphasizing its potential to promote social cohesion and counter radical ideologies in academic environments.
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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 | Systematic review | high |
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 | 0.023 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.224 | 0.232 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".