Capitalizing on Blockchain Technology for Efficient Crowdfunding: An Exploration of Ethereum's Smart Contracts
Bibliographic record
Abstract
Blockchain technology, the bedrock of cryptocurrency, has evolved beyond its initial scope, paving the way for a plethora of decentralized, secure applications.The anticipation surrounding blockchain's potential to become the dominant technology orchestrating online transactions is growing, due to its ability to provide efficient and secure solutions for a diverse range of applications on a global scale.This study delves into the potential benefits of deploying blockchain technology in the realm of crowdfunding.In recent years, crowdfunding has emerged as an alternative route for startups to garner funds, presenting a less bureaucratic and simpler process.The conventional crowdfunding model entails a collective of individuals contributing minor sums to support a project or start-up, with the crowdfunding platform earning a commission to coordinate the needs of both funders and fundraisers.Nonetheless, blockchain technology could potentially enhance the crowdfunding process by introducing a decentralized, tamperproof system comprised of interconnected nodes, thereby bolstering transparency, trust, efficiency, and convenience.To realize this potential, this paper proposes the application of Ethereum smart contracts to tackle prevalent issues in both Donation-Based and Equity-Based crowdfunding models.By adopting this approach, we hope to bring about greater transparency and efficiency to the crowdfunding process, thereby fostering an environment of trust that may catalyze further innovation in this space.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".