D2.3 – Bi-annual reports on harmonisation progress
Bibliographic record
Abstract
INSTAR Deliverable D2.3 provides an update on the progress of the INSTAR European Task Forces (ETFs) in harmonising European standardisation efforts across key technology areas. D2.3 describes the progress on the setup of the INSTAR International Task Forces (ITFs), i.e., joint committees that are being established by INSTAR with partners identified in the selected geographical regions of Australia, Canada, Japan, Singapore, South Korea, Taiwan, USA. The deliverable then provides an analysis of the development of the INSTAR harmonisation roadmaps. These roadmaps align European standardisation priorities with international Standards Developing Organisations (SDOs), including ISO, ETSI, IEEE, ITU, IETF, and CEN-CENELEC. The key findings are: Engagement in standardisation across regions: Japan, USA, and South Korea show strong involvement in standardisation across all knowledge areas. Singapore and Canada focus on quantum technologies, AI, and cybersecurity. Australia and Taiwan engage selectively in IoT, 6G, and environmental monitoring. The initial ETF-ITF coordination has proven effective, ensuring a structured approach for international standardisation collaboration. Standardisation priorities: Interoperability, cybersecurity, and AI-driven automation are top concerns across all domains. 6G, AI, cybersecurity, and quantum technologies require strong international partnerships. Global cybersecurity frameworks and quantum communication protocols need harmonisation to ensure cross-border trust and security. Standards for energy efficiency and sustainability in 6G, Cloud-Edge-IoT, and AI must be further developed. Regulatory alignment (e.g., AI Act, Cyber Resilience Act, Data Act, eIDAS) with global partners is crucial for seamless integration. Engagement with INSTAR: An MoU has been signed with South Korea’s Telecommunication Technology Association (TTA) focusing on initial cooperation towards the workstreams of Data, CEI, and Cybersecurity. An MoU will shortly be signed with Japan’s Japan Business Council Europe (JBCE) focusing on 6G, AI, among others. INSTAR has successfully established a strategic framework for standardisation harmonisation, enabling stronger international engagement and alignment with global SDOs. The next phase will focus on accelerating roadmap adoption, deepening partnerships, and addressing key standardisation challenges in emerging digital technologies.
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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 | 0.066 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.061 |
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".