Assessing the divide between humans and the natural world: effects of increased experience in natural areas
Bibliographic record
Abstract
There is speculation as to whether humans are able to comprehend, appreciate, and protect natural environments when they have received minimal or no exposure to such areas. There are many speculative explanations for this bifurcation between humans and nature; however, there is an absence of a solution to address the issue. Current research strongly emphasizes the health benefits of receiving more exposure to nature, especially since much of North America has witnessed a dramatic shift towards a more technologically driven culture that is heavily reliant on the urban environment. \nThis study investigates the declining connections between humans and the natural world, and the effect nature-based experience has on individual perspectives regarding stewardship and environmental awareness. Utilizing qualitative research methods, interviews were conducted with nine participants of three different Outward Bound Canada expeditions in order to determine whether a trip of one week or longer had influenced participants? sense of stewardship and/or environmental connectivity. \nResults demonstrate a positive correlation between participant exposures to isolated natural environments and an increased sense of environmental commitment or stewardship, especially with regard to forming connections with nature, willingness to participate in environmental-based volunteer initiatives, and mitigating fear of the outdoors.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".