Perception of cognitive distance : effects of physical environment on the perception
Bibliographic record
Abstract
The lack of knowledge concerning the relationships between the physical environment and people's behavior was discussed in relation to the inadequacy of today's architectural programming. Possible applications of man-environment studies were suggested. Nature of the environmental information was analyzed, and a term 'cognitive distance' was defined. Essential variables that affect the construction of the mental map and the perception of distance were derived primarily from previous studies. The city of Vancouver was analyzed utilizing methods originated by K. Lynch, and a survey was conducted in the city to examine relations between the various variables and the perception of distance. The type of the distance examined was 'inward (toward downtown)’, 'ego-centric', and urban scale(4 mile) 'cognitive distance'. Results: Following four variables seemed to have effects on the perception of distance; sex, age, mode of transportation, and the score on 'Thing-orientation scale'. Male tended to be more accurate in their estimations (or to estimate the particular distance of 4 mile as being shifter) than female Ss. Ss aged over 20, or car drivers, or Ss with higher T-scores were also more accurate than younger Ss, or bus riders or car passengers, or Ss with lower T-scores, respectively. Concerning the possible effects on perception by physical variables, our results were unclear mainly due to the differing characteristics of the Ss at the four locations. Implications of results were discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".