Bibliographic record
Abstract
Summary Adrian Smith joined The Alan Turing Institute as Institute Director and Chief Executive in September 2018. In May 2020, he was confirmed as President Elect of the Royal Society. He is also a member of the government's AI Council, which helps boost AI growth in the UK and promote its adoption and ethical use in businesses and organisations across the country. Professor Smith's previous role was Vice‐Chancellor of the University of London where he was in post from 2012. He is a past President of the Royal Statistical Society and was elected a Fellow of the Royal Society in 2001 in recognition of his contribution to statistics. In 2003‐04 Professor Smith undertook an inquiry into Post‐14 Mathematics Education for the UK Secretary of State for Education and Skills and in 2017, on behalf of Her Majesty's Treasury and the Department for Education, published a 16‐18 Maths Review. In 2006 he completed a report for the UK Home Secretary on the issue of public trust in Crime Statistics. He received a knighthood in the 2011 New Year Honours list. The following conversation took place at the Alan Turing Institute in London, on July 19 2019.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.041 | 0.017 |
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".