Design and Develop the Business Process Model for Open Access Copyright Management System Using Permission Less Blockchain
Bibliographic record
Abstract
Copyright management is essential in the digital economy for protecting intellectual property rights.With the exponential growth of digital content, existing centralized systems struggle with inefficiencies, enforcement challenges, and a lack of transparency.Blockchain technology offers a decentralized and tamper-proof solution, enabling transparent, traceable, and legally compliant copyright management.However, achieving a standardized, trusted, and interoperable platform remains a key challenge.This study presents an optimized blockchain-based copyright management model that enhances process coordination and automation.The approach utilizes a structured "Chain of Transformation", converting an optimized state transition model into intelligent smart contracts.Key stages include: (1) defining the business process state transition model, (2) optimizing it using the Processes States and Transition Reduction Algorithm (PSTRA), (3) transforming it into smart contracts, and (4) refining these contracts into intelligent contracts.This structured automation ensures secure, efficient, and enforceable copyright management.Comparative analysis with existing systems highlights improvements in decentralization, security, cost efficiency, process communication, and standardization.The integration of intelligent smart contracts enhances automation, ensuring transparent and legally enforceable copyright execution.The model also strengthens security against copyright infringements by leveraging blockchain's immutable ledger for verification and enforcement.The proposed blockchain-based model improves copyright protection by providing a secure, automated, and legally compliant framework.It fosters innovation by standardizing copyright processes, reducing inefficiencies, and lowering operational costs.Future research will focus on enhancing scalability, cross-chain interoperability, and legal adaptability to address evolving industry needs.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".