Malaysia’s National Blockchain Roadmap: A Critical Discourse Analysis of Focus, Goals, and Challenges
Bibliographic record
Abstract
This Critical Discourse Analysis (CDA) study examines Malaysia’s National Blockchain Roadmap 2021-2025, assessing its focus, goals, future development plans, and challenges based on text and context analysis. The study reveals that the roadmap presents a comprehensive strategy aimed at leveraging blockchain technology to drive economic growth, foster innovation, and enhance Malaysia’s competitiveness in the digital age. It emphasizes government adoption, regulatory clarity, industry collaboration, talent development, infrastructure investment, and use case exploration to lay the foundation for Malaysia to emerge as a global leader in blockchain innovation. Through strategic initiatives and partnerships, Malaysia aims to streamline administrative processes, enhance transparency, and optimize resource allocation within public agencies, showcasing its commitment to digital transformation and socioeconomic development. Additionally, the roadmap prioritizes the development of a robust regulatory framework to govern blockchain usage, fostering innovation while ensuring compliance. Industry engagement is underscored with initiatives aimed at catalyzing the development of industry-specific blockchain solutions tailored to address key challenges and opportunities. The roadmap anticipates further development and expansion driven by technological advances, evolving regulatory frameworks, and industry collaboration. Based on the context analysis, the roadmap faces several challenges, including regulatory uncertainty, interoperability issues, resource constraints, lack of trust, intellectual property concerns, and competing priorities. To overcome these challenges, Malaysia needs to advance in technology, regulatory frameworks, industry collaboration, education, talent development, infrastructure investment, and use further case exploration.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".