Reducing Rural Car Ownership: Cultural Not Policy Changes?
Bibliographic record
Abstract
Automobile use is recognized as affecting public health, environmental sustainability, land use, and household expense. Car use is closely tied to car ownership rates. Most car ownership research focuses on urban areas; however, 97% of the United States’ land area and a fifth of its population remains rural. Factors that affect car ownership in these communities may be different than in more urbanized areas. This research focuses on the 2,285 counties in the continental United States that are defined as entirely rural by the guidelines established in the Agricultural Act of 2014. These counties were grouped by five multi-state regions using U.S. Census Bureau definitions. Their percentage changes in car ownership, as well as other demographic variables, over a quarter century were calculated using data from the 1990 Decennial Census and the 2014 5-Year American Community Survey. A multiple regression model was estimated for each grouping to identify counties with lower-than-expected changes in car ownership. For each grouping, one of these outlying counties was selected and matched with another county whose changes in car ownership were within expected ranges given demographic developments. Local professionals were then interviewed to identify policies possibly responsible for the difference in car ownership trends between the matched-pair counties. The interviews suggested that, contrary to expectation, transportation policies had no discernable effect on rural car ownership, but land use polices and, more often, cultural factors linked to changing populations were associated with reduced rural car ownership.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".