Bibliographic record
Abstract
Abstract Chapter 12, “Digital Identity: Exploring a Consumer-Centric Identity for Open Banking,” authored by Greg Kidd, Founder & CEO of GlobaliD, with an exploration of open banking and digital identity. Customer verification is one of the major challenges in open banking. Kidd explores the concept of digital identity, beginning with the history of identity verification and the different kinds of attestations used today. Then the author describes digital IDs and what problems they can solve. Today, some countries have responded to the need to easily authenticate people in a digital economy by implementing centralized public IDs, such as India’s Aadhaar, Estonia’s e-residency, and Singapore’s Singpass. In fact, India’s Aadhaar is the world’s largest single biometric identity system. Other countries have private models, including Canada’s Verified.Me and Sweden’s BankID and Freja eID+. These private models tend to be more decentralized and can use different kinds of attestation to verify someone’s identity. Using the notion that the individual controls his or her data, Kidd then proposes design principles for self-sovereign digital identities that can be controlled by the individual and examines market challenges for making this a reality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.064 |
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".