An operational framework relating generic activity patterns in the residential open space environment to physical design
Bibliographic record
Abstract
Behavioral research is providing meaningful information with respect to the relationship between human activities and physical design of the residential environment. While the appeal among professions and social scientists for its input into the design process seems unanimous, the failure to pool, simplify and constantly update such data, continues in it being accessible to, and usable by, only a small, enlightened and privileged minority. On the other hand, a large share of today's housing in Canada is produced by individuals and organizations, many of whom are generally familiar and concerned with neither human behavior nor basic design principles. Resultant projects invariably betray an almost single-minded approach, that of realizing a maximum number of dwellings at a minimum expenditure on amenities. This study attempts to narrow the gap between the researcher and the practitioner. It proposes a communicable, organized approach to designing and evaluating physical components in the residential open space environment, as to their responsiveness to generic human activities. A Frame of Reference (activities and components) is developed, which generates the context and the problem for Patterns, which, in turn, suggest solutions or platforms for discussion. The principle evolved, is then applied to site plans of three recently completed housing projects. Variables, in this model, include age of users and climate of the location. The benefits of this approach, include prevention of the worst of open space planning, while encouraging good work to proceed. Avenues for implementing such a process are briefly explored and its application, by money-lending agencies, held as feasible.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".