Bibliographic record
Abstract
The innovation of the Metaverse heralds a new milestone in the Information Age as investors move forward with the plan to bring the metaverse to fruition. The metaverse will offer a heightened experience in terms of interactivity, economics, and platform, while paving the way for greater immersion through virtual reality and augmented reality technologies. It is likely that as the metaverse develops, gaming will offer a unique social experience through its features such as virtual worlds. Based on this, it is important for policymakers to look at extremist subcultures that will operate in the metaverse through these virtual features. Due to the role played by fringe subcultures in facilitating the recent mass shooting event in Buffalo, this article aimed to examine the main features of the metaverse and how its immersive properties could influence the creation of future metaversal subcultures that could act as a gateway towards future mass shooting incidents. To that end, it applied the model of malevolent creativity to the extremist use of online spaces to gain insight on how such properties could aid online extremists towards mobilization. Results show that the concatenation of malevolent creativity, innovation, and subcultural extremism may bridge the gap between ideation of mass shootings and mobilization. Based on this, the implication of this research suggests that tech entrepreneurs for the metaverse should be mindful of the risks that disconnection from the real-world society can create for young, isolated users and aim to implement safeguards in integral areas of the metaverse seven-layer chain, such as spatial computing, discovery, and the creator economy.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".