Bibliographic record
Abstract
We published the Smart Jurisdictions Index in November 2018. This compared 48 jurisdictions across the globe using 151 factors, ranking a number of pioneering States in the USA at No 1, with France at 10, Canada at 11, the Netherlands at 12, the UK-England at 13, and Switzerland at 18. Drivers include how jurisdictions handle Identity, Documentation, Legal, and Payments, and how nations are beginning to deploy services to citizens using Smart Ledger technology. We have built on the Smart Jurisdictions Index to develop the Smart Centres Index. The Smart Jurisdictions Index was created using a range of instrumental factors divided into two groups and covering nine relevant areas: Base Factors: Business Environment Reputational Infrastructure Human Capital Financial Services Smart Ledger Factors: Legal Documentation Identity Payments. As a result of feedback on the pilot, we have developed our approach into the new Smart Centres Index, which uses the same range of instrumental factors, but which focuses on financial and business centres rather than jurisdictions; and which widens the scope of research to cover the regulation, depth, and quality of new technologies.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".