Affect, Architecture and Water: Bibliometric Analysis of the Literature
Bibliographic record
Abstract
Effects of water in the space is the subject of study in many scientific fields. The research question is that whether the water features are included in the studies carried out the intersection of affect and architecture. Therefore, the purpose of this study is to explore the literature regarding the concepts of affect-architecture-water and to determine the concepts in current research areas and the primary authors. The scope of this study consists of documents in Scopus database. The keywords “Affect, Affective, Architecture, Water” were selected for the systematically analysed scan made in Scopus database. Scanning was done by creating three different combinations with the selected keywords. First combination is “Affect” and “Architecture; Second combination is “Affective” and “Architecture”; Third combination is “Affect”, “Architecture” and “Water”. After collecting the bibliometric data of a total of 1557 documents according to three different combinations from the database on November 21, 2022, the downloaded data files were transferred to the VOSviewer (1.6.18.0) software. Bibliometric analysis with science mapping techniques was applied to the dataset by the VOSviewer. Firstly, Scopus analysis search results were examined. Secondly, Visuals were created by science mapping techniques. As a result, nine concepts and 26 authors were determined. The concepts for the gaps are “Architecture, Affect, Atmosphere, Perception, Space, Sensory Experience, Architectural Design, Built Environment, Emotion”. The authors are “Deleuze, Guattari, Davidson, Anderson, Barrett, Damasio, Kraftl, Lyubomirsky, Manzo, Massumi, P. L. Russell, Wigley, Scherer, J. A. Russell, Böhme, Abusaada, Matteis, Bachelard, Merleau-Ponty, Pallasmaa, Plutchik, Watson, Zumthor, Lefebvre, Sørensen, Ebbensgaard”.
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
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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | 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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.250 | 0.262 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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.
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".