Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Chris Skinner was born in London on 12 March 1953. He completed a BA in mathematics in 1975 at the University of Cambridge. He then obtained an MSc degree in statistics from the London School of Economics and Political Science (LSE) in 1976 and worked as an assistant statistician in the Central Statistical Office for 1 year. After working as a research assistant in LSE from 1977 to 1978, he joined the University of Southampton as a lecturer in 1978, where he earned a PhD in social statistics in 1982. He remained at the University of Southampton, where he became a senior lecturer in 1989 and professor of statistics in 1994. While serving as the head of his department from 1997 to 2000, he played a crucial role in the creation of an MSc programme in official statistics in 1999. In 2011, he returned to the LSE, where he currently holds the position of professor of statistics. Chris is the author of over 80 peer‐reviewed articles in statistical journals and the co‐editor of two influential books on the analysis of survey data. He made significant research contributions covering areas that include the analysis of survey data, inference in the presence of non‐response and measurement errors and statistical disclosure control. He served on several advisory committees, including the Statistical Methods Advisory Committee at Statistics Canada (from 2000 to 2011) and the National Statistics Methodology Advisory Committee in the United Kingdom (from 2001 to 2010). He has received numerous awards and honors for his outstanding contributions to survey sampling and social statistics. He is a Fellow of the American Statistical Association, Fellow of the British Academy and a Fellow of the Academy of Social Sciences. In 2009, he received the West Medal from the Royal Statistical Society for contributions to social statistics, and in 2010, he was made a Commander of the Most Excellent Order of the British Empire. In 2019, he also received the Waksberg award to recognize his contributions to survey methodology. The following conversation took place at LSE on 21 May 2019.
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.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 it