“Where Do Children Go?”: Exploring Children’s Daily Destinations With Children, Parents, and Experts
Bibliographic record
Abstract
Research on children’s destinations has primarily focused on school trips, yet their lives are more than that. Different destinations contribute to children’s quality of life in different ways, but this is rarely examined. For our research, focus groups were conducted with different stakeholders to better understand non-school destinations, namely by identifying common, daily, and informal destinations and perceptions of how they relate to children’s well-being. Online focus group discussions were conducted with children (aged 8–12), parents (with children aged 7–13), and experts from different cities across Canada in May and June 2023, to obtain diverse opinions about children’s destinations. The analysis was conducted based on a prior review to categorize children’s destinations, identify informal destinations, green and grey places, and the relation between those destinations to children’s well-being. Discussions with parents, children, and experts highlighted the diversity of destinations relevant to children. Leisure destinations were one of the most mentioned in the discussions. Spaces without specific rules or structures were identified by experts as beneficial for children’s cognitive, social, physical, and psychological health. Parents mentioned primarily formal places, whereas children and experts mentioned primarily informal ones. Green destinations were more associated with physical well-being, though children dominantly associated green destinations with psychological well-being as well. All groups dominantly associated grey-type destinations with social and cognitive well-being. Using these results, urban planners can develop strategies to improve children’s access to their daily destinations that support their well-being.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".