A comparative study of the perception of traditional villages between different media
Bibliographic record
Abstract
Nowadays, more and more people choose to go to the traditional villages with beautiful scenery and relaxing atmosphere to relax and relieve stress due to the unique cultural background and environmental atmosphere of the traditional villages, which well met the public's aesthetic value of the landscape environment. Previous research has primarily focuses on traditional villages' history, cultural background, rural land planning, traditional ancient architecture and other directions. However, relatively few studies have explored the public perception of the traditional village landscape. Existing studies are usually limited to a single-field study or laboratory study, and the medium used to show the village landscape remain relatively simple. Therefore, this study investigates the perceptions of traditional village landscape space using an experimental approach. It examines how subjects perceive landscapes using two-dimensional (2D) displays, three-dimensional (3D) VR displays, and five media representing landscapes: real scenes, photographs, videos, 2D panoramas, and 3D panoramas. The advantages, limitations, and feasibilities of various combinations of media and medium representations are analyzed to determine the optimal methods for landscape perception and evaluation. The results demonstrate that the medium combination significantly influences landscape evaluations by affecting the subjects' perceptions. The degree of landscape perception is a highly significant mediating factor. The combination of 3D displays of 720° panoramas provides the best agreement between laboratory-based findings and real-world environments, regardless of the media type. This paper summarizes the data of public perception of village landscape under various media, compares and analyzes the results, and aim to provide the most suitable research reference scheme for future studies in related fields.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".