Children mapping their realities and aspirations: an innovative methodological tool with implications for practice, program and policy
Bibliographic record
Abstract
This presentation examines an innovative transdisciplinary Children's Mapping Project in the evolving work of the National Association of Child Care Workers (NACCW) in South Africa, and the engagement of child and youth care workers (cycws) of the NACCW Isibindi-Ezikoleni (Safe Parks/Courage in Schools) school-based program in locational and aspirational mapping practice with children and youth in seven provinces across South Africa, in collaboration with the Geomatics and Cartographic Research Centre (GCRC), Carleton University, and Circle of All Nations, the Legacy work of Indigenous Elder William Commanda. This innovative project initiated in January 2022 engages social services sector workers, researchers and children from a diversity of locations (urban, rural, informal settlements and isolated), with a range of environmental challenges (floods to drought) and community and social challenges (safety, poverty, empowerment) in cartography and a locational exploration of a complex intersection of issues. The presentation will include a preliminary analysis of the children's map visualizations of environmental and social conditions, and a prioritization of issues for further practice, program and policy development, in partnership with researchers and national and international organizations like the South African Human
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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 | 0.059 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".