Deciphering landscape preferences: Investigating the roles of familiarity and biome types
Bibliographic record
Abstract
Although people generally have positive evaluations of natural environments and stimuli, theory and research suggest that certain biomes are more preferable than others. Existing theories often draw on evolutionary ideas and people’s familiarity with biome types, with familiarity being the most supported, albeit not conclusively, in existing research. Across three samples (n = 720) we sought to compare preference ratings of 40 images that represented ten biomes (beach, lake, tropical and temperate forest, marsh, swamp, meadow, park, mountain, and river). We addressed objective familiarity by recruiting samples from two distinct geographies (Florida and Ontario), and we assessed subjective familiarity via image ratings. Familiarity was positively associated with liking biomes, though this trend was stronger for subjective familiarity compared to geography. Substantial variation in biome type preferences could not be attributed to familiarity. Specific biome types were strongly preferred irrespective of familiarity and geography. e.g., beaches and lakes were highly preferred, while marshes and swamps were substantially less preferred than other biome types. Further analyses found that the individual difference of nature relatedness predicted both familiarity and liking of all biomes except beaches, and that there was a lack of seasonal effects (fall and winter) across two Ontario samples. We discuss how results provide qualified support for the familiarity view, limits of this interpretation, how methodological choices such as the number of ratings might impact findings, and the potential applications of these results in landscape design.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".