Tokenized Stocks for Trading and Capital Raising
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper examines the concept of tokenizing assets on public permissionless blockchains such as Ethereum, Algorand or Avalanche. It starts with an overview of the core principles and components of public blockchains, such as the ownership attribution and efficient transaction processing. The paper argues that tokenization could simplify and streamline back-office operations, enable new interactions between issuers, financial firms and investors, and allow novel service models in digital asset issuance and management. The paper then examines the functions and potential usage of tokens, comparing and contrasting traditional and digital assets. It also discusses the mechanisms for token issuance and potential issues that may arise from tokenizing existing assets. The challenges and advantages of digital assets for implementing traditional asset functions such as dividend payments, shareholder voting, and shareholder communications are also discussed. Finally, the paper covers the usage of tokenized assets and the potential effects of smart contract services on existing financial service providers. The paper suggests several best practices and requirements for token issuance, including a token registry, standards for backed or asset-linked tokens, and a failsafe reconciliation process if the blockchain fails.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it