“Where Do Children Go?”: Exploring Children’s Daily Destinations With Children, Parents, and Experts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it