Crypto/Space: Computational parasitism, virtual land grabs, and the production of Web3 Exit zones
Bibliographic record
Abstract
This paper explores how so-called ‘Web3’ blockchain projects are materially and socially constituted. A blockchain is an append-only distributed database. The technology is being hyped as applicable for a whole range of industries, social service provisions, and as a fix for economic disparities in communities left behind by mainstream financial systems. Drawing on case studies from our ongoing research we explain how, despite being virtual, Web3 projects are dependent on clearly defined spaces of production from which they derive their speculative value. We conceptualise this relationship as Crypto/Space, where space and blockchain software are mutually constituted. We consider how Crypto/Spaces are produced in three ways: 1) how project developers are adopting a parasitic relationship with host locations to appropriate energy, infrastructure, and local resources; 2) how projects enable ‘virtual land grabs’ where developers are engaging in land acquisitions, and associated displacement of local people, with no real intention to use the land for the declared purpose; and 3) how blockchain technology and speculative finance imaginaries are inspiring new anarcho-capitalist crypto-utopian ‘Exit zones’, often in the Global South. Far from being a zero-sum virtual game world, we argue that cryptocurrency projects are parasitic, often requiring predation on poor and otherwise marginalised communities to appropriate resources, onboard new users and enable favourable regulation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".