A Tale of Peaks and Valleys: Sinusoid Relationship Patterns Between Mountainousness and Basic Human Values
Bibliographic record
Abstract
Mountains—mythic and majestic—have fueled widespread speculation about their effects on character. Emerging empirical evidence has begun to show that physical topography is indeed associated with personality traits, especially heightened openness. Here, we extend this work to the domain of personal values, linking novel large-scale individual values data ( n = 32,666) to objective indicators of altitude and mountainousness derived from satellite radar data. Partial correlations and conditional random forest machine-learning algorithms demonstrate that altitude and mountainousness are related to increased conservation values and decreased hedonism. Effect sizes are generally small (| r| < .031) but comparable to other socio-ecological predictors, such as population density and latitude. The findings align with the dual-pressure model of ecological stress, suggesting that it might be most adaptive in the mountains to have an open personality to effectively deal with threats and endorse conservative values that promote a social order that minimizes threats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".