The Metaverse as the Digital Leviathan: A Case Study of Bit.Country
Bibliographic record
Abstract
As Bitcoin continued to make headlines in 2021, additional digital assets such as non-fungible tokens brought more users into the blockchain ecosystem. As more individuals and entities took a closer look at the use cases for blockchain technology, the term metaverse began to emerge across news outlets and social media platforms. With Mark Zuckerberg, the Chief Executive Officer of Facebook, announcing that the organization would become a metaverse company and change the organization’s name to Meta, this announcement came with some criticism in that the Meta metaverse would be centralized. In this case study, the current state of nation-states was viewed through the lens of Hobbes’ The Leviathan to assess whether decentralized metaverses will transition to a Digital Leviathan using Bit.Country - a metaverse within the Polkadot blockchain ecosystem. The case study was conducted through interviews and uncovered that the quadruple bottom line implemented in conventional business could be transferrable to a digital world built on various blockchains, non-fungible tokens, and governance in a Digital Leviathan governed by the people.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".