Bibliographic record
Abstract
With the continuous upsurge of virtual and augmented reality, technology is seen precipitously evolving introducing new innovations that would have been formerly unbelievable. One of these innovations is the metaverse, a distinctive and immersive virtual world, a network of 3D virtual environments resided by avatars of actual people that focuses on social connections. This virtual world would continue to evolve and develop based on consumers’ choices and interactions within this space, synchronized with the real world that has no end. The metaverse can be described as an indefinite universe that continues to swell as more and more users are involved, merging reality and virtuality in one. In the field of digital advertising and marketing, advertising agencies and strategists need to keep up with the speed of the latest artificial intelligence developments, with a full understanding of the metaverse and its potential. Keeping in mind the main target audiences, Gen Z and millennials, as they have been already spending time in virtual worlds and participating in a range of metaverse behaviors through virtual games such as Roblox, and other virtual reality technologies. This research aims to explore the potential of advertising within the metaverse universe, the challenges it would face, the virtual strategies that can tie in with the real world, and how brands can forge their own virtual pathways in relation to consumer behavior.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.039 | 0.037 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".