Are Population and Land Use Changes Perceived as Threats to Sense of Place in the New West? A Multilevel Modeling Approach
Bibliographic record
Abstract
Abstract The Intermountain West's rapid changes in population growth and land use may be welcome to some, but others perceive such changes as threats to sense of place. The objective of this study is to assess whether New West and Old West contextual variables predict how agricultural landowners view threats to agricultural lifestyles and sense of place. We analyze survey data collected from 2,270 agricultural landowners in Colorado and Wyoming utilizing a multilevel regression model (MLM). We posit that this analytical approach is effective for evaluating hierarchal New West or Old West economic configurations that may otherwise be difficult to observe. Our study specifically examines whether population pressures threaten agricultural lifestyles in the amenity‐based New West or in Old West economic regimes with proclivity toward large‐scale agricultural production. Our results show that landowners in farming‐dependent counties and in high‐amenity areas express greater concern than other landowners surveyed about increases in population growth that could threaten an agricultural way of life. Furthermore, we demonstrate that these perceptions relate to whether individuals reside in New West or Old West counties. In summary, some of the contextual variables of New West and Old West economic structures predict whether individuals perceive population growth and land use changes as threats to sense of place.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".