Does Adding Local Tree Elements into Dwellings Enhance Individuals’ Homesickness? Scenario-Visualisation for Developing Sustainable Rural Landscapes
Bibliographic record
Abstract
Rural residential settings are important elements of livable and sustainable rural areas across the world. Enhancing people’s attachment to these landscapes through fostering feelings of homesickness could help in the pursuit of better rural residential settings. We studied homesickness, an emotion found to be associated with higher place attachment and quality of life, related to rural landscapes in southeast China, looking specifically at the presence and configuration of rural dwellings and trees. We used Photoshop to manipulate different configurations of typical rural dwellings and trees, and three series with twelve types of landscape scenes were generated. We looked at the following six emotional factors linked to homesickness: naturalness; regional culture; identity; psychology; experience; and landscape aesthetics. The analytic hierarchy process (AHP) and semantic differential (SD) methods were used to evaluate the level in which the landscape evoked feelings of homesickness amongst study participants, i.e., a group of university students from different disciplines. Results show that the homesickness emotional response level was higher in most of the simulated landscapes, as compared to the original landscape, and that response levels differed significantly between the three types of visualized landscape configurations. The emotional response level showed differences for manipulated landscape scenes with twelve different trees added to dwellings. Through cluster analysis of the results, we divided trees into three grades of emotional response for each dwelling type. Adding trees thus was found to change the emotional response to the landscape, and different tree configurations with different types of dwellings results in different responses. The study shows that careful design of the rural landscape can help build stronger emotional relations of humans with their local environment, which is a key ingredient for sustainable countryside living.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".