The Beyond-Human Natural World: Providing Meaning and Making Meaning
Bibliographic record
Abstract
Much academic and media attention has been focused on how nature contributes to psychological health, yet, most of this focus has been on happiness or hedonic well-being. Although numerous writers and researchers have linked connecting with nature as a pathway to meaning in life, an integrated overview has not yet (to our knowledge) been offered. Our manuscript is thus of both theoretical and practical importance with respect to finding meaning in life. In this hybrid commentary/review paper, we examine the link between meaning in life and relating to the beyond-human natural world. Through presenting supportive empirical research and interdisciplinary insights, we make the case that connecting with the natural world provides us with meaning in various ways. We discuss how nature is a common source of meaning in people's lives and how connecting with nature helps to provide meaning by addressing our need to find coherence, significance/mattering, and purpose (the three aspects comprising the tripartite model of meaning life). We also consider how connecting with nature enhances our experiential appreciation for life, a fourth aspect of meaning in life recently proposed. Our discussion then expands to examining nature as a place of attachment. Going beyond how nature provides us with meaning, we consider how engaging in nature-based activities provides an avenue for many people to build meaningful lives. We close by considering how threats to nature are a threat to meaning in life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".