socio-ec(h)o: Ambient Intelligence and Gameplay
Bibliographic record
Abstract
This paper describes the preliminary research of an ambient intelligent system known as socio- ec(h)o. socio-ec(h)o explores the design and implementation of an ambient intelligent system for sensing and display, user modeling, and interaction models based on game structures. Our interaction model is based on a game structure including levels, body states, goals and game skills. Body states are the body movements and positions that players must discover in order to complete a level and in turn represent a learned game skill. The paper provides an overview of background concepts and related research. We describe the game structure and prototype of our environment. We discuss games research concepts we utilized and our approach to group user models based on Richard Bartle’s game types. We explain the role of embodied cognition within our design and elaborate on what we chose to encode as embodied actions, cognition and communication. We describe how we utilized selective responses that were real-time, gradient, provided rewards and were unique to different group user models. We introduce our approach to designing ambient intelligent systems that is ecologically inspired. We stress the empirical nature of the design work and the role of participatory design in developing our system.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".