The Impact on Shaping Mental Maps of the Urban Typology, in the Context of Transportation Network; Toronto
Bibliographic record
Abstract
A mental map is also described asa mental model that is formed in the minds of users, which, in basis, providesway and direction finding. A mental map summarizes all icons the user has inmind about a place. Studies show that mental maps areformed based on environmental characteristics as well as the user’scharacteristics. This study approaches the effects of topography and itsconsequent environmental features, such as transportation networks, on mentalmap typology. Appleyard defends, that the usersperceive the environment by the transportation system and therefore a mental mapof an environment is shaped by its transportation system. In his research, hedescribes the types of mental maps based on this survey, and additionalresearch conducted on mental maps also supports these findings. However, it wasdetermined that, in settlements, formed organically by topography and thesettlement culture, with difficult to grasp transportation networks, the mentalmaps were forming according to easy landmarks instead of the transportationsystems. In this study, various findingsrelated to the mental map research has been tested on a gridiron layout set ona flat surface. The city of Toronto has been selected as a case study becauseof her gridiron transportation network laid out plain land. The research isbased on observation and a survey which contains written and drawingstatements, and was conducted in Toronto University. In this survey, theparticipants have been asked to draw Toronto’s mental map as well as to givesome personal information about themselves. The results have proved that thetransportation systems have an effect on the type of mental maps created in theusers’ minds. 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".