BEYOND THE LENS: A BIBLIOMETRIC JOURNEY THROUGH ARCHITECTURAL PHOTOGRAPHY RESEARCH
Bibliographic record
Abstract
This study presents a bibliometric analysis to map academic trends, collaborations, and thematic developments in architectural photography using VOSviewer and Biblioshiny software. Based on 470 publications retrieved from the Web of Science (WoS) database, the analysis explores the intellectual structure of architectural photography research through keyword co-occurrence, citation networks, and bibliometric coupling. The findings reveal a significant increase in academic interest over the past two decades, highlighting the growing interdisciplinary connections of the field with cultural heritage, digital visualization, and artificial intelligence applications. Cluster analysis identified eleven major thematic groups, including cultural heritage documentation, digital photogrammetry, AI-assisted visualization, and media representation. Country-level bibliometric coupling analysis shows that the United States is a dominant research hub with strong links to the United Kingdom, Canada, and Australia. At the same time, China and European countries demonstrate intensive regional collaborations. The study concludes that architectural photography is no longer limited to traditional documentation practices but is increasingly evolving into a research domain shaped by emerging technologies such as 3D modeling, LiDAR, and augmented reality. These findings offer valuable insights into emerging research directions such as computational photography, immersive visual storytelling, and interdisciplinary approaches to architectural documentation.
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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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.137 | 0.204 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".