Initial Crypto-asset Offerings (ICO), tokenization and corporate governance
Bibliographic record
Abstract
This interdisciplinary article discusses the potential consequences due to distributed ledger technology (DLT), tokenization as well as the emergence of new kinds of firm stakeholders, ie the crypto-assets holders, on the governance of small and medium-sized enterprises (SMEs) as well as of publicly traded companies. Since early 2016, a new way of issuing assets and raising funds has rapidly emerged as a major issue for FinTech founders and financial regulators. Frequently referred to as initial coin offerings, initial token offerings (ITO), token generation events (TGE) or simply ‘token sales’, we use in our article the terminology initial crypto-asset offerings (ICO), as it describes more effectively than ‘initial coin offerings’ the vast diversity of assets (utility tokens, security tokens, crypto-currencies) that could be created and which goes far beyond the sole payment instrument issue.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.065 | 0.023 |
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; both teacher heads agree on what is shown here.
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".