Bibliographic record
Abstract
Blockchain-based security token offerings (STOs) provide a new way of crowdfunding and corporate financing. Tokens are immediately transferable and can be traded 24/7 on secondary markets, clearing and settlement is a matter of only a few minutes, tokens can be held personally, i.e. brokers and custody accounts are no longer required and the underlying blockchain ensures transparency of all transactions. This study provides an overview of security tokens and the STO model for corporate financing. Our analysis investigates security tokens from the perspective of a firm looking to raise capital. Building on signaling theory, this paper examines 1) whether companies conducting an STO make use of cheap signals to influence investment behavior and 2) if such use of cheap signals is effective. We analyze a dataset of 151 STOs and identify that cheap signals of human capital and social media are used by projects and have a positive effect on funding success. The type of signals influencing funding success indicate that the market is still immature, as projects have a clear incentive to enlarge the level of asymmetric information between them and potential investors. The anticipated level of punishment for misusing cheap signaling is low, as the mechanism does not represent fraud but "cheating". This is a concern for investor protection.
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.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".