MetaGadget: An Accessible Framework for IoT Integration Into Commercial Metaverse Platforms
Bibliographic record
Abstract
While the integration of Internet of Things (IoT) devices in virtual spaces is becoming increasingly common, technical barriers to controlling custom devices in multiuser virtual reality (VR) environments remain high, particularly limiting new applications in educational and prototyping settings. We propose MetaGadget, a framework for connecting IoT devices to commercial metaverse platforms that implements device control through HTTP-based event triggers without requiring persistent client connections. Through two workshops focused on smart home control and custom device integration, we explored the potential application of IoT connectivity in multiuser metaverse environments. Participants successfully implemented new interactions unique to the metaverse, such as environmental sensing and remote control systems that support simultaneous operation by multiple users, and reported positive feedback on the ease of system development. We verified that our framework provides a new approach to controlling IoT devices in the metaverse while reducing technical requirements and provides a foundation for creative practice that connects multiuser VR environments and physical spaces.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".