The aesthetic value of natural vegetation remnants, city parks and vacant lots: The role of ecosystem features and observer characteristics
Bibliographic record
Abstract
Physical and mental well-being are linked to exposure to ecosystems perceived as aesthetically pleasing.However, the perceived aesthetic value is shaped by both ecosystem features and observer characteristics.This study explores the relationships between these two influential factors, and perceptions across three Canadian urban greenspace types: natural vegetation remnants (forest stands, marshes and peatlands), city parks, and vacant lots.We administered an online photo-questionnaire, completed by 514 respondents, who assessed the beauty of 45 greenspaces on a five-point Likert scale.Respondents were university students and staff, members of the general public, and specialists in ecosystem services.They also answered questions on socio-demographic factors, proenvironmental attitudes, nature-related travel habits, and ecological knowledge.We calculated five variables from each greenspace photo: vegetation structural complexity, visual complexity, percentage of green pixels, percentage of canopy cover, and presence/absence of artificial structures.On average, natural vegetation remnants and city parks were preferred over vacant lots.Among natural remnants, forests were preferred over semiopen peatlands.Different ecosystem features were associated with perceived beauty across the three greenspace types.For natural remnants and city parks, visual complexity was positively related to appreciation scores, whereas for vacant lots, the presence of artificial structures was associated with lower appreciation scores.Respondents who self-identified as environmental defenders, had greater knowledge of flora, and travelled to observe fauna gave higher appreciation scores to vacant lots compared to other respondents.Younger respondents, and those who engaged in frequent nature-related travelling attributed higher scores to natural remnants.We conclude that aesthetic preferences for natural vegetation remnants, city parks, and vacant lots are influenced by a complex interplay of ecosystem features and observer characteristics.Understanding these intricate relationships can guide the management of vacant lots and promote more inclusive engagement with greenspaces, by considering the varying preferences of diverse segments of the population.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".