“Even in the Middle of the City, I Notice Nature Around Me”: Is Nature Relatedness Associated with Noticing More Nature in Urban Environments?
Bibliographic record
Abstract
Nature relatedness is an individual difference characteristic defined as an interconnected feeling between the self and nature (Nisbet et al., 2009).Previous meta-analytic research suggests that nature related individuals are happier (Capaldi et al., 2014;Pritchard et al., 2020).The literature suggests nature related individuals may be happier because they are more likely to go out into nature, which promotes well-being.However, it could also be partly because nature-related individuals may be noticing more nature around them at any given moment, which has not been previously tested.In this dissertation, I examined the possibility that highly nature related individuals notice more nature in urban scene images.Items of the Nature Relatedness scale (Nisbet et al., 2009) propose noticing nature behaviours that fit in the self-concept of a person high on the trait.Still, no research has explored behavioural measures of momentary noticing nature in this association.In this dissertation, noticing was explored through two distinct processes, perception and attention, with the research split into two phases.Perception (Phase 1) is characterized as what an individual interprets as nature in an urban scene.Attention (Phase 2)is operationalized in two ways: the first objects seen in an image (first attention) and voluntary attention paid to pleasant objects in the images.I hypothesized that nature related individuals would perceive more nature, notice nature first, and focus more on the nature in the images than individuals lower on nature relatedness.Contrary to the hypothesis, there was no association between perception of nature and nature relatedness in Phase 1.However, there was an association between attention being paid towards nature and nature relatedness in Phase 2, where nature related individuals had their attention drawn to nature first and chose to focus their attention on it.There is also some preliminary support that nature related people's ability to notice nature may be a reason they are happier in general.This study is the first to utilize iii behavioural tasks to explore if nature related individuals do, in fact, notice more nature in urban environments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".