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
Two problems, spurious regression bias and naive data mining, conspire to mislead analysts about predictive models for stock returns. This article demonstrates the two problems, how they interact, and makes suggestions for what to do about it. If expectations about a stock's return are dependent through time, then variables like dividend yields and yield spreads can appear to be better at predicting returns than they actually are. This is a potentially serious problem when implementing tacticalassetallocationstrategies, activelymanagingaportfolio, measuringinvestment performance, attempting to time the market, and in other situations where analysts use lagged variables to predict returns. We show that searching for predictor variables using historical data can increase the likelihood of finding a variable with spurious regression bias. Such a variable appears to have worked in the past, but will not work in the future. A simple transformation of the predictor variables can be used to reduce the risk of finding spurious predictive relations.
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 | 0.052 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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