Bayesian Inference and Posterior Simulators
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
This paper was prepared for invited presentation at the American Agricultural Economics Association meetings, Chicago, August 2001. Partial financial support from National Science Foundation grant SES‐9731037 is gratefully acknowledged as well as Xavier Irz's able translation of the abstract. Recent advances in simulation methods have made possible the systematic application of Bayesian methods to support decision making with econometric models. This paper outlines the key elements of Bayesian investigation, and the simulation methods applied to bring them to bear in application. Les récents développements dans les méthodes de simulation rendent possible l'application systématique de l'approche Bayésienne afin de développer des outils économétriques d'aide à la décision. Cet article présente les éléments essentiels de l'approche Bayésienne ainsi que les méthodes de simulation utilisées lors de son application.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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