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
Citation (2006), "List of Contributors", Fomby, T.B. and Terrell, D. (Ed.) Econometric Analysis of Financial and Economic Time Series (Advances in Econometrics, Vol. 20 Part 2), Emerald Group Publishing Limited, Bingley, pp. xi-xii. https://doi.org/10.1016/S0731-9053(05)20036-1 Publisher: Emerald Group Publishing Limited Copyright © 2006, Emerald Group Publishing Limited Book Chapters Contents Dedication List of Contributors Introduction Good Ideas The Creativity Process Realized Beta: Persistence and Predictability Asymmetric Predictive Abilities of Nonlinear Models for Stock Returns: Evidence from Density Forecast Comparison Flexible Seasonal Time Series Models Estimation of Long-Memory Time Series Models: a Survey of Different Likelihood-Based Methods Boosting-Based Frameworks in Financial Modeling: Application to Symbolic Volatility Forecasting Overlaying Time Scales in Financial Volatility Data Evaluating the ‘Fed Model’ of Stock Price Valuation: An out-of-sample forecasting perspective Structural Change as an Alternative to Long Memory in Financial Time Series Time Series Mean Level and Stochastic Volatility Modeling by Smooth Transition Autoregressions: A BAYESIAN Approach Estimating Taylor-Type Rules: An Unbalanced Regression? Bayesian Inference on Mixture-of-Experts for Estimation of Stochastic Volatility A MODERN TIME SERIES ASSESSMENT OF “A STATISTICAL MODEL FOR SUNSPOT ACTIVITY” BY C. W. J. GRANGER (1957) Personal Comments on Yoon's Discussion of My 1957 Paper A New Class of Tail-dependent Time-Series Models and Its Applications in Financial Time Series
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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