EmotoTent
Bibliographic record
Abstract
EmotoTent is an interactive socio-emotional learning system developed in response to escalating levels of violence, inequality and marginalization in schools seen in the early 21st Century. The system is inspired by advances in biosensing wearables, tattoo displays, brain sensors, robotic agents, artificial intelligence (AI), gestural interaction and 3D holographic displays. By 2030, technological advances will enable us to prototype and investigate questions related to experiential and embodied emotional learning; emotion-based human-computer interaction, affective biosensing, empathetic AI agents, and 3D interactive holographic environments. We envision EmotoTent as a modular, emotion-sensing Holodeck. In the EmotoTent program children learn and practice emotion regulation and empathy with peers, pets and a robotic dog agent in ways that are experiential, embodied and playful. We propose EmotoTent as a core element of a K-6 socio-emotional learning curriculum designed to improve school culture through the enhancement of children's ability to regulate emotions and interact with human and non-human species with empathy and compassion. Enhancing these qualities has been shown to lead to reductions in violence and bullying, racism, gender inequality and other forms of marginalization. We predict that the EmotoTent socio-emotional learning program will improve school cultures and create a foundation for children's lifelong well-being.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
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".