Tokenization of everything? Exploring the limits of blockchain technologies in the governance of financial markets and assets
Bibliographic record
Abstract
Abstract We examine the implications of tokenization for the transformation of things into financial assets. Framed as the ‘democratization’ of financial investment by its advocates, tokenization is a process whereby asset ownership is fractionalized and represented by a digital token to be sold to potential investors on blockchain-based platforms. Tokenization can be seen as an extension of securitization to illiquid real-world assets or digital assets; as such, tokenization is often framed as a technique to isolate risks, reduce financing costs, and generate returns without selling the underlying assets. For example, real estate security tokens offer fractionalized ownership to smaller investors through digital means lowering entry barriers, though such investors still typically lack exposure to diversified real estate token portfolios. Through an analytical and empirical investigation, we argue the governance claims made about tokenization obscure a key contradiction: tokenization is touted as a way to democratize financial markets, but the necessary adaptation of tokenization to prevailing financial market infrastructures undermines this democratization promise. Engaging with this contradiction, we unpack the governance of financial markets and assets through the techno-financial transformation of things into digital tokens, focusing on the promise of tokenization to democratize finance.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".