Spatial Cognition and Perception of Large Scale Object Constellations: Evidence from Search Domain Analysis in Urban Environment
Bibliographic record
Abstract
Spatial Cognition and Perception of Large Scale Object Constellations: Evidence from Search Domain Analysis in Urban Environment ˇ ¸ ilters Jur´ gis Sk Center for Cognitive Sciences and Semantics, University of Latvia, 1A Lomonosova Street Riga, LV 1019 Latvia G ¸ irts Burgmanis Faculty of Geography and Earth Sciences, University of Latvia, 10 Alberta Street Riga, LV 1010 Latvia Zaiga Kriˇs j¯ ane Faculty of Geography and Earth Sciences, University of Latvia, 10 Alberta Street Riga, LV 1010 Latvia Linda Apse Faculty of Humanities, University of Latvia, 4A Visval?a Street Riga, LV 1050, Latvia Abstract: Our study explores perception of urban environment and its encoding into the system of spatial prepo- sitions. We explore the divergences between the physical topology and the cognitive topology of environment con- strained by (a) geometric invariants (Landau & Jackendoff, 1993), (b) functional knowledge (Coventry & Garrod, 2004), (c) reference frame (Levinson, 1996), and (d) individual’s spatial experience linked to a variety of social factors. Our results suggest that processing spatial environment generates a mental map containing different scopes of search domains for prepositions where the scope depends on the location of the living place (home) of the test person. We propose the following correlation: the more peripheral is the home of a test person the bigger is the scope of the search domain, e.g. the region denoted by ”near the house” is larger for those who live in the periphery of a city.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".