Stephen Elliott Fienberg 1942–2016, Founding Editor of the <i>Annual Review of Statistics and Its Application</i>
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
Stephen Elliott Fienberg was the founding editor of the Annual Review of Statistics and Its Application. Steve had an outsized personality and a passion for statistical science that was quite unique, and he combined these with his legendary energy to provide a remarkable level of leadership for the statistical science community, and a sweeping vision of the importance of statistical arguments for science, health and policy. The editorial team of the Annual Review of Statistics and Its Application is working hard to carry on his legacy for the journal. In this article we highlight some of his contributions through the voices of his students and collaborators. It is by no means a comprehensive assessment of his scholarship, but we hope it provides a window into his impact and influence on several generations of scholars. As Reid & Stigler (2017) wrote in Volume 4, “his lasting imprint on the science of statistics and its application defies simple categorization.”
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
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