eDelphi study & consultation on Metaverse standards development: deliverable D1.3 of the shaping the Metaverse (phase II) project
Bibliographic record
Abstract
This study utilised a modified Delphi 3-stage approach to gauge expert perspectives on our research question: What should the priority actions be for policy makers or regulators in the development of interoperable standards for metaverse & virtual worlds? We recruited a pool of participants with a range of metaverse interoperability and standards expertise based in UK, Europe and the US, covering core technology and application areas as well as human-centred perspectives. Our study indicated an emerging consensus that: 1st The UK should develop and promote a position on metaverse standards and interoperability. With three additional areas of emerging consensus around the following actions within a UK context: 2nd Promote use of open, accessible pre-existing standards over proprietary solutions for interoperable virtual worlds. 3rd Identify use cases and interoperable technology solutions. 4th Increase engagement between academia, industry and government around solutions for interoperability. Qualitative and quantitative data gathered from our Delphi study expert working group indicates that the UK has a vital role to play in the development of interoperable, virtual world technologies and practices, but that there is a lack of vision around addressing potential challenges and opportunities. Our data also suggest that to realise the benefits of interoperability, an international approach needs to be adopted, where the UK could contribute within existing international frameworks, while developing distinctive strengths and providing connected leadership without duplicating international efforts.
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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 | 0.093 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".