Realizing Open Space Conservation: A Cross-State Survey of Perceptions and Preferences Within Residential Developments
Bibliographic record
Abstract
The conversion of open space to residential development increasingly continues across the United States, impacting both humans and nature. Residential development requires public input to generate meaningful places and understand contextually relevant priorities. Most municipal policies do not guarantee the provision of open spaces when residential development occurs, missing opportunities for benefits to those communities and reducing both environmental and spatial justice. This study operated a seven-state verbal questionnaire to collect and analyze a small-sample population perceptions concerning open space conservation and green space preferences towards future residential development priorities. Statistical analytical results indicated patterns, trends, and relationships within data. Although 46% of United States residents living in rural, suburban, and urban community types believe the amount of open space required in new developments should be determined on a case-by-case basis, just under half believe that requiring at least 50% open space in new developments is appropriate. More than half of Americans in the states targeted, particularly Colorado and liberal-leaning respondents, believe a lack of coherent planning will prevent open space conservation and that open space planning and conservation should be a priority for city governments. Beyond the United States, this study provides research and insight into conservation strategies that foster healthier landscapes and living environments globally.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".