Developing children’s innate systems intelligence to enhance social and emotional learning
Bibliographic record
Abstract
This paper explores the concept of systems intelligence in children and highlights its links to social and emotional learning (SEL), proposing that young people possess an innate capacity to understand interdependence, relationships, and change within complex systems. The argument centers on the importance and feasibility of cultivating systems intelligence in children and youth as a response to the complex challenges societies face today. Traditional education often overlooks this innate capacity, emphasizing technical academic content and reductionistic approaches instead. Drawing on insights from Daniel Goleman and Peter Senge’s The Triple Focus (2014), we propose that integrating attention to intrapersonal, interpersonal, and broader social and ecological interdependence can deepen and extend SEL. Practical examples of systems intelligence in action—such as young children using feedback loops to resolve conflicts—illustrate the intuitive nature of this form of thinking. We also show that a variety of practical tools now exist to help educators weave systems science into SEL in diverse contexts. Finally, we situate cultivating systems intelligence within recent educational innovations, arguing that, together, these can enable a transformative educational paradigm that fosters both deeper understanding of interconnectedness and agency in addressing global issues.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".