The Concept and Implementation of Perceptual Regions as Hierarchical Spatial Units for Evaluating Environmental Sensitivity
Bibliographic record
Abstract
Abstract: Studies of human spatial behavior and life spaces are useful because they allow a better understanding of the relationship between people and environment. In an era when there is growing public pressure to understand this relationship, studies of life spaces may provide insight into the environmental sensitivity of different groups of people. In this article, we propose a method for characterizing life spaces based on perceptual factors and show how the method can be used to explore the sensitivity of humans to environmental quality and to study human spatial behavior in the form of residential choice. In particular, the perceptual regions that we introduce constitute new hierarchical spatial units of analysis that join location to activities, the two key concepts of life spaces. The structural and environmental differences of the perceptual regions in relation to the sociological characteristics of urban and suburban behaviors are explored for two districts within the city of Québec (Canada). The approach offers the potential for developing some interesting applications in urban planning: the means to be more sensitive to the wishes of individual households in decisions concerning urban spaces, and a tool to assist persons evaluating different residential locations. It is noted that the analyses performed may be largely automated.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".