Bibliographic record
Abstract
Spatial knowledge will always be a complicated and ambiguous issue.Urban legibility is one of the most important issues related to spatial knowledge.The spatial knowledge and mental images are affected by the dynamic nature of urban spaces, and this fact shaped the definition of the legibility of urban spaces.Accessibility of urban paths and linking urban nodes increase clarity and image ability in urban areas.The frequent use of urban space, especially paths, enhances the ability to read visual elements, so ease of access is a key variable in determining the percentages of use of the path and type of movement in it.The importance of this subject arises from the need to re-create "place" within the urban spaces of contemporary cities, specifically urban links and public paths that direct to and link main landmarks, as the paths represent a large percentage of urban spaces within the centers of contemporary cities.This study aimed to build a model of assessment that expresses the relationship between the accessibility of urban paths and their legibility.This study assumes that legibility is related by its objective aspect to accessibility and aims to measure legibility in urban links ("River of Hila as a case study").The suggested model tries to express the relationship between the accessibility of urban paths and their legibility.This will be reviewed through three levels of intersected measurements that link legibility and accessibility.According to the results of testing the levels of intersected indicators, path [C-3] achieved the highest values according to the survey and questionnaire in the field study.The methodology adopted to achieve the main goal of this research is divided into two parts: 1) Determining the procedural definitions of the study vocabulary, visual legibility and accessibility.2) Determining the basics of the conceptual link between the concepts of visual legibility, and accessibility.3) Building the intellectual model that will regulate the measurement framework and its indicators.4) The final part of this methodology will be the framework structure of intersection indicators and the results of the applied study.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".