Bibliographic record
Abstract
What does probability mean?"This question echoes in the entire scientific work on foundations of probability, evidence theory and mathematical statistics produced by Glenn Shafer.2016 is the 40th anniversary of the publication of "A Mathematical Theory of Evidence" and I thought that The Reasoner's readers might be interested in getting a glimpse on the personal steps that led its author from Dempster-Shafer rule of combination, to his game-theoretic foundations of probability.Glenn Shafer obtained his PhD in mathematical statistics from the University of Princeton in 1973; after teaching Statistics at Princeton, he returned to Kansas in 1976 to teach Mathematics at the University of Kansas.In 1984, he moved from Mathematics to Business at Kansas.He joined the Rutgers Business School in 1992 and served as its dean from 2011 to 2014.The Dempster-Shafer theory has been extensively applied in engineering and artificial intelligence as well as in accounting, and "A Mathematical Theory of Evidence" is one of the most cited books in the history of statistics, also widely so outside the discipline.
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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".