Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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 it