Children's Cognitive Perception of their Neighborhood: Reading the Cognitive Maps of Children from Al-Wihdat Palestinian Refugee Camp in Amman, Jordan
Bibliographic record
Abstract
In urban spaces, children constitute a major segment of those growing up there, and children illustrate their ideas on the cities they live in, how they perceive their neighborhoods as users of the urban environment. Their drawings can be seen as and interpreted as cognitive maps, and can serve as an important source for today's urban designers, architects, educators, and government officials. The cognitive maps of children are not only some visual productions but are also imbued with the realties and objects of everyday life, like an organically designed textile. There is a relative lack of studies on the children's relationship with their particular urban space and its range of elements. This study seeks in part to address that knowledge gap. Although reading cognitive drawings is, in fact, a psychological subject, as they contain cognitive perception of the space they engage with and are related to the external world, they are particularly of interest for the planners and designers creating and seeking to change and better that environment. Based on what the drawings display, what needs that human-focused urban design suggests as reflected in children's cognitie mapping will be interpreted in the scope of this study. The material for this research has been extracted from my 2004 doctoral dissertation and constitutes one of a series of four related papers in progress discussing children's outdoor environment using four different methodological approaches. This paper investigates and seeks to interpret the drawings of their environment by a selected number of students 10–14 years old from Al- Whidat Palestinian refugee camp in Amman, Jordan, their cognitive perception of that living environment and the unhealthy chaotic aspects of the 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".