BCT Crowdfunding: Is It the Bridge of Trust Required for Funding EU’s SMEs?
Bibliographic record
Abstract
Small and medium-sized enterprises (SMEs) are the cornerstone of the European eDaconomy, representing 99.8% of all businesses and providing 66% of employment. Despite their critical role, SMEs face significant challenges in accessing traditional financing, particularly in the aftermath of the 2008 financial crisis, which led to a reduction in riskier lending by banks. Crowdfunding has emerged as a viable alternative, offering a decentralized and democratized avenue for raising capital, especially through platforms powered by blockchain technology.This paper explores the potential of blockchain technology (BCT) to revolutionize crowdfunding within the European Union (EU), addressing the critical financial needs of SMEs. BCT enhances transparency, trust, and efficiency in crowdfunding by enabling features such as tokenization, smart contracts, and decentralization. These innovations offer solutions to longstanding issues in traditional finance, such as fraud, information asymmetry, and the reliance on intermediaries.However, the paper also highlights the limitations and challenges of crowdfunding in Europe, particularly the disparities in crowdfunding trends between the UK, Nordic countries, and the rest of the EU. Financial data from 2018 and projections for 2023 reveal that while the number of crowdfunding campaigns in the EU is growing, the per-campaign value remains significantly lower compared to the UK, reflecting a continued focus on smaller-scale investments.The integration of BCT into crowdfunding practices presents both opportunities and obstacles. Although it offers a promising path to more efficient and secure funding mechanisms, the successful implementation of BCT will require coordinated efforts from governments, regulatory bodies, financial institutions, and technology developers to navigate the complex legal and technological landscape.In conclusion, while blockchain-based crowdfunding has the potential to reshape SME financing in Europe, realizing its full benefits will demand proactive engagement with emerging challenges and continuous adaptation to evolving regulatory frameworks.
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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 | 0.012 | 0.052 |
| 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 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".