Bibliographic record
Abstract

 
 
 
 Financial mathematics must make use of assumptions in the development of mathematical models that provide predictive power on the behavior of economic markets, as it is impossible to collect data on the market as a whole. As a result, important quantities, such as the risk-measurement of a portfolio, are often inaccurately estimated. The financial market seems to be an erratic, pattern-less system. Indeed, attempts to find patterns, and to explain the processes behind the price movements of an asset, have been largely unsuccessful. This is analogous to the ‘Turkey Problem' described by N. Taleb in his book "The Black Swan". To illustrate, a turkey spends its life being fed and raised for slaughter, a fact that is unbeknownst to it. From the point of view of the turkey, life is delicious and predictable, until the day it is killed. For the turkey, its death is a ‘black swan event’, as it represents something highly unpredictable and catastrophic. This same type of uncertainty is also present in financial markets.
 
 
 
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".