Trees are honest, bugs are creative, sunsets are hopeful - Identifying character strengths in nature: A structured tabular thematic analysis
Bibliographic record
Abstract
The psychological construct of nature connectedness has been consistently linked to well-being and pro-nature behavioral outcomes, with a sense of self considered important for individuals to feel like they are part of nature. Interventions focusing on noticing good things in nature and the Five Pathways Framework have been utilized to help people reconnect with the more-than-human world although they have often overlooked incorporating nature within the self-concept and emphasizing similarity with nature despite its importance for the construct. We developed and tested a related, but alternative, approach to previous interventions to focus on similarity and sense of self through anthropomorphism: that of mindfully identifying how one's own character strengths are exhibited in nature. A Structured Tabular Thematic Analysis was conducted on 747 written observations (n = 93) of shared character strengths in nature. Five themes were generated: (1) finding representations of the self through seasonal change; (2) identifying with weather and the character strengths it possesses; (3) experiencing awe and wonder in nature through shared character strengths; (4) nature as an honest or dishonest entity; and (5) the inability to find similarity between oneself and nature. These themes provide insight into the ability of the intervention to enable participants to find a sense of self in the rest of nature when identifying shared strengths. Nature connectedness pathways of meaning, compassion, and beauty were also evident in the observations. Implications for using a character strengths-based approach to boost nature connectedness through a shared sense of self and similarity are discussed. The identification of personal character strengths shared with nature offers a new and meaningful way to reconnect with the more-than-human world to which we belong.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".