Mapping the literature on augmented reality-based gaming: a text mining approach
Bibliographic record
Abstract
Purpose This study aims to focus on carrying out a quantitative analysis of publications related to augmented reality (AR)-based gaming. AR combines the real world with the digital world and is a large basis of data in an ICT-based digital society. Therefore, there is a need for a comprehensive bibliometric review of research papers on AR-based gaming to identify the potential for the advancement of knowledge in this field. Design/methodology/approach In the experimental part, reference data from Scopus was extracted according to a search string formulated with great care. The retrieved data were then analyzed through open-source, sophisticated bibliometric tools, VOSviewer, along with Biblioshiny-R. Findings After running the analysis, it turns out that the years with the biggest number of publications were in 2010 and 2012. The focus areas of study include mixed reality, surveys and helmet-mounted displays. The USA was the most connected country in the research collaboration network; it has strong links with other nations including the UK, Canada and Greece, which cluster around it in the map. This trend of monetarily increasing number of papers depicts the growing interest and sustainable productivity in this research domain. Originality/value The purpose of this paper is to present a novel perspective on AR-based gaming, which has not been so far examined by bibliometric analysis. The results provide important information for researchers and stakeholders. In addition to filling a void in the literature, this study is an important reference for those researchers who are interested in exploring evolution and trends in AR-based gaming research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".