Without Assumptions: Development of a Socio-Emotional Learning Framework That Reflects Community Values in Cameroon
Bibliographic record
Abstract
Socioemotional learning (SEL) skills are the competencies that children need to be successful and accepted members of society. In this study, we built a SEL framework and a SEL measurement tool from the ground up that assess children's development of skills with communities of the Baka ethnic group in Cameroon. We conducted a participatory and interactive study to develop a SEL framework and measurement tool that is specific to the context of indigenous Baka communities in Cameroon. Using a quick ethnography methodology and an emic approach, a researcher team comprised mainly of Baka community members engaged parents, teachers, and others in iterative cycles of data collection, analysis, and reflection to develop the framework and assessments. The resulting Baka SEL framework includes skills and domains distinct from predominant SEL frameworks, underscoring the importance of drawing SEL priorities from communities themselves. Shared foundational constructs underlying the Baka SEL framework and other frameworks indicate possible universal human expectations for emotional and relational skills. Two SEL measurement tools were produced: a caregiver tool and a teacher tool, each using storytelling to elicit specific, honest, and detailed information about child behavior. These tools allow us to capture child behavior in the school and the home, and to collect data on all participating children within a specific time period. The described approach is a simple, practical, and culturally appropriate strategy for collaborating with rural communities to articulate their understanding of SEL. The resulting framework and tools illustrate the importance of rooting SEL in local culture, while the approach to developing them serves as a model for other early childhood care and education organizations and programs.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".