Metaverse and Marketing: Metaverse - Another Marketing Tool for Brands to Promote Their Products and Services
Bibliographic record
Abstract
As we spend our lives online, it's becoming increasingly difficult to distinguish the "real" life from a life that is lived virtually surrounded by digital devices. Today, a smartphone is not just a device we use, but it's become a place where we live. The real-life routines, interests and obsessions of consumers today are heavily imitated in the digital worlds: from driving cars in the virtual world, selecting outfits for digital avatars to wear, to cultivating virtual relationships and intimacy (New Trend Report: Into the Metaverse, Wunderman Thompson Intelligence, 2021). As technology progresses, humankind is rapidly moving from the age of social media and mobile devices to the world of web3 with mixed reality experiences. Historically, brands have always moved where consumers have been, to engage, promote and create enriching experiences for their consumers. Thus, this research paper highlights how brands and businesses will use the metaverse as another marketing tool and focuses on the use of the Metaverse by brands to promote their products or services for an eventual purchase in the real-life physical world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".