Children’s cognitive story mapping: a complex South Africa/Canada transdisciplinary collaboration
Bibliographic record
Abstract
This innovative transdisciplinary children's cognitive story mapping collaboration was initiated in 2022 by Circle of All Nations (CAN), Geomatics and Cartographic Research Centre (GCRC) Carleton University, (in Canada), National Association of Child Care Workers (NACCW) and Durban University of Technology (DUT) (in South Africa). It integrates approaches from arts and humanities, social services and cartography in child and youth care work by engaging social service sector workers and researchers in art story map creation with children and youth. The joint engagement and research in the compilation and presentation of the data, findings and knowledge is leading to new dimensions in participatory mapping where children initiate the map creation process with workers. The children's map visualizations of social and environmental realities, concerns and needs have led to a prioritization of issues for practice, program and policy development, including in child protection case management. Researchers complement the work with national and provincial digital maps that permit analysis and focussed interventions. This article introduces the term Cognitive Story Maps; it is a preliminary exploration of theoretical frameworks, including Indigenous, that support collaborative bridge building between distinct domains of creative visualization, methodological practice and cartographic representation to generate innovations in knowledge creation and research.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.051 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".