Designing for human-nature connectedness: A case study of the Barefoot Path Spalt, Germany
Bibliographic record
Abstract
There is a need to bridge the disconnection between humans and nature through conscious landscape architectural designs. Recreation infrastructure can be a tool to guide humans towards a stronger connection with nature, improving health, welfare, and care for environmental issues. Considering Haraway's (2016) work on making kin with the more-than-human other and being with the trouble through cognisant presence of the self within a natural world, it appears that progress towards human-nature connectedness is stunted by commodification of the natural environment. This is reflected in nature discourse, and a risk perception driven by fear of nature which has become the unknown. A mixed methods study of the Barefoot Path Spalt, Germany, implementing document analysis, interviews, and criticism incorporating autoethnography and expert field assessments, generated knowledge on how trail designs can improve human-nature connectedness. The study based its findings on three major trail attributes, environmentally conscious trail design, the barefoot component, and interactive equipment and art. Results show that an intrinsic connection with the environment can be targeted with intentional trail design that entices curiosity, gradually guiding participants to build human-nature connectedness, contesting false risk perception. Results show that well designed trails have the potential to enhance human-nature connectedness with relatively cost-effective implementations. Future trail development and upgrades should consider (1) Conscious integration of a trail into the natural environment through a low-impact approach. (2) Using a creative feature that delivers a unique, nature-centric experience, for example through sensory immersion. (3) Installing both play equipment and art to mask the border between trail and context environment makes use of, and encourages more traffic to a trail, generating positive nature-contextual experiences.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".