The State of the Electronic Identity Market: Technologies, Infrastructure, Services and Policies
Bibliographic record
Abstract
Authenticating onto systems, connecting to mobile networks and providing identity data to access services is common ground for most EU citizens, however what is disruptive is that digital technologies fundamentally alter and upset the ways identity is managed, by people, companies and governments. Technological progress in cryptography, identity systems design, smart card design and mobile phone authentication have been developed as a convenient and reliable answer to the need for authentication. Yet, these advances ar enot sufficient to satisfy the needs across people's many spheres of activity: work, leisure, health, social activities nor have they been used to enable cross-border service implementation in the Single Digital Market, or to ensure trust in cross border eCommerce. The study findings assert that the potentially great added value of eID technologies in enabling the Digital Economy has not yet been fulfilled, and fresh efforts are needed to build identification and authentication systems that people can live with, trust and use. The study finds that usability, minimum disclosure and portability, essential features of future systems, are at the margin of the market and cross-country, cross-sector eID systems for business and public service are only in their infancy. This report joins up the dots, and provides significant exploratory evidence of the potential of eID for the Single Digital Market. A clear understanding of this market is crucial for policy action on identification and authentication, eSignature and interoperability.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".