Bibliographic record
Abstract
Abstract Hadi et al. ( Journal of Consumer Psychology , 34, 2023) have created a masterful and wide‐sweeping review of the consumer behavior literature on the Metaverse. They envision our encounter with the Metaverse as a consumer journey. In this commentary, I highlight some of their unique contributions and suggest additional insights that emerge when we view the digital frontier as a liminal place betwixt and between now and then, here and there, and reality and virtuality. The Metaverse is also a metaphor and I entertain three metaphoric interpretations. First, the Metaverse is an experience machine of the sort that Robert Nozick imagined in his thought experiment involving real and artificial pleasures. Alternatively, consumers themselves can be seen as desiring machines as Gilles Deleuze and Féliz Guattari characterized them, and the Metaverse can be seen as an instantiation of our collective desires. Or thirdly, the Metaverse can be regarded as a shared hallucination. As these diverse metaphors suggest, imagining the metaverse is a projective exercise. But the consequences may involve up to a trillion dollars in revenues, so I hope these provocations prove useful whether they are ultimately borne out or not.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".