Post-event report - Shaping International Standards in Advanced ICT Tech Regulation An Introduction to INSTAR and its Global Impact
Bibliographic record
Abstract
In the EU policy context, standardisation plays a pivotal role in shaping regulations that safeguard end users while fostering market-based competition and interoperability of complementary products and services. The EU Standardisation Strategy underscores the necessity for the EU to assert a leadership position in global standardisation processes. This strategic approach not only enhances cooperation with relevant initiatives sharing similar regulatory philosophies but also aligns with broader EU objectives such as protecting EU strategic interests and promoting EU rules as global standards. An essential element of the strategy is "multilateral engagement" through Europe’s Digital Partnerships and the EU-US Trade and Technology Council (TTC), which involves collaborating with international partners to forge a consensus on international standards. This cooperation is crucial for maintaining economic security and reinforcing mutual resilience, particularly in a complex geopolitical environment. Thus, the broader framework of multilateral engagement is exactly where INSTAR plays a crucial role. INSTAR is an EU-funded project that aims to support the implementation of Europe’s Digital Partnerships and the EU-US TTC by working together with relevant entities from Australia, Canada, Japan, Singapore, South Korea, Taiwan and the USA to drive international common standards for AI, Cybersecurity, Digital ID, Quantum Technologies, IoT, 5G, 6G and Data Technologies. To do this, INSTAR will establish expert Task Forces to analyse and identify European standardisation priorities and legislation in these areas, share best practices and provide recommendations to be taken in to foster greater international cooperation and enhance EU's strategic positioning in the global tech governance landscape. The webinar featured Emilio Davila Gonzalez (EC DG CNECT Policy Officer, Head of ICT Standardisation Sector) who highlighted the strategic role that INSTAR and its Task Forces will play in sharing European best practices and priorities in order to facilitate international dialogue on standardisation. As the INSTAR project currently sets up the Task Forces, the webinar was an opportunity to target and attract potential members who are essential in shaping the future of standardisation in these technology fields.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.028 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".