Bibliographic record
Abstract
The authors believe that the Metaverse is a paradigm shift infrastructure technology that may make 60% of universities irrelevant in the next 20 years if they don’t move away from the meta-studies model. Meta-studies refers to an archival, largely paper-based process of “teaching” what other people have written about, focused on the past, and supported by the “publish or perish” imperative. The Covid-19 pandemic has forced universities into adopting technologies that form a part of the Metaverse, but for the most part they have used these technologies to perpetuate the status quo. If universities cannot change their practices to become more agile, adopting design thinking, inquiring minds, and critical thinking not only for students but also for themselves as institutions, it will be commercial entities such as Meta and Microsoft who will decide what education will take place in the Metaverse, and students will go there whether universities are present or not. It is therefore important that universities change their own paradigms, and engage with this technology to discover how it can be used effectively in education, so as to foster principles and values for building knowledge and cementing ethical, sustainable practices.
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 | 0.035 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.030 | 0.055 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".