ENVIRONMENT AND BEHAVIOR / September 2001Brunson et al. / APPROPRIATION OF DEFENSIBLE SPACE RESIDENT APPROPRIATION OF DEFENSIBLE SPACE IN PUBLIC HOUSING Implications for Safety and Community
Bibliographic record
Abstract
chology from the University of Illinois. She currently works as a consultant at Cogem Research in Montreal, Canada. Her work focuses on the design of virtual environ-ments and on the effects of schools and communities on adolescent development. FRANCES E. KUO is an assistant professor at the University of Illinois in the de-partments of Natural Resources and Environmental Sciences and Psychology. Her re-search examines impacts of the environment on healthy human functioning in individ-uals, families, and communities. WILLIAM C. SULLIVAN is an associate professor at the University of Illinois in the departments of Natural Resources and Environmental Sciences and Landscape Architecture. His research focuses on the psychological and social benefits of urban nature, and on citizen participation in environmental decision making. ABSTRACT: Defensible space (DS) theory proposes that the built environment can promote neighborhood safety and community by encouraging residents ’ appropria-tion of near-home space. This article examined the relationship between three differ-ent forms of resident appropriation and residents ’ experiences of neighborhood safety
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".