Urbanization and Land‐Use Change: A Human Ecology of Deforestation Across the United States, 2001–2006
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drawing from human ecology, the present study sheds light on the ways in which urbanization drives changes in forest cover at the local level across the continental United States. Using county‐level data from the National Land Cover Database and other US governmental sources, the area of forest cover lost in the construction of the built environment between 2001 and 2006 is regressed on the size , density , and social organization of a locality. Controlling for several other factors, estimates from spatial regression models with two‐way fixed effects show that increasing density slowed down deforestation, while variables representing size and social organization had the opposite effect. Based on these results, urbanization is framed as a multidimensional human ecological process with countervailing impacts on the natural environment.
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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 | 0.000 | 0.000 |
| 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 it