Examining Children's Perceptions and Use of Their Neighbourhood Built Environments: A Novel Participatory Mapping Approach
Bibliographic record
Abstract
This thesis uses a mixed methods approach to contribute towards a more complete understanding of the relationship between the built environment and children’s active school travel. It is argued that active travel –human powered transportation – to and from school provides regular physical activity that can help reverse rising rates of overweight or obese Canadian children. The built environment of a child’s school neighbourhood has been shown to influence travel decisions. To achieve higher rates of children’s active travel, a comprehensive understanding of the built environment is required.\nThis study uses child-led perceptual mapping (CLPM) and GIS analysis in a case study with children from three elementary schools in London, Ontario to determine how perception and use of their school neighbourhood varies according to the built environment. The typology for the perceptual mapping activities was inspired by that of urban theorist Kevin Lynch, with children identifying destinations, zones, and routes. A high degree of participation is attained according to Roger Hart’s Ladder of Participation.\nIt is observed that children perceive of their neighbourhood in unique and complex ways. Children did not perceive land uses by the designated function, nor did they define land uses in singular ways. Prominent features were also not confined to the proximity around their school. Layering the child directed methods as an effective tool in understanding issues facing children’s active travel. It is concluded that active travel must be a priority cemented in policy, and that stakeholders should engage in participatory research across disciplines if rates of active travel are to increase. CLPM is a novel approach that should be used by researchers who aim to understand children’s perception and use of the built environment. Children deserve a major stakeholder role in the school travel planning process, and further research on the impact of the built environment on children’s active travel is needed.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".