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
Abstract The Wilkie stochastic investment model, developed by Professor Wilkie, is described fully in two papers; the original version was published in 1986 and the model was reviewed, updated, and extended in 1995. In addition, the latter paper includes much detail on the process of fitting the model and estimating the parameters. It is an excellent exposition, both comprehensive and readable. It is highly recommended for any reader who wishes to implement the Wilkie model for themselves, or to develop and fit their own model. The Wilkie model is commonly used to simulate the joint distribution of inflation rates, bond yields, and returns on equities. The 1995 paper also extends the model to incorporate wage inflation, property yields, and exchange rates. The model has proved to be an invaluable tool for actuaries, particularly in the context of measuring and managing financial risk. In this article, we will describe fully the inflation, equity, and bond processes of the model. Before doing so, it is worth considering the historical circumstances that led to the development of the model.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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