Bibliographic record
Abstract
In this study, Nathan Balke and Mine Yucel ask whether the Eleventh Federal Reserve District's Beige Book description contains timely information about economic activity within the District. They examine whether the Beige Book description tracks current Texas real gross state product (GSP) growth and current Texas employment growth. They also study whether the Beige Book has information about growth not present in other regional indicators that would have been available to analysts at the time of the Beige Book's release. They find that both the Beige Book summary and the average across sectors reflect Texas GSP and employment growth very well. These two measures of the Beige Book also have predictive content for one quarter ahead GSP growth. Balke and Yucel also find that the Eleventh District's Beige Book has information content for Texas economic activity over and above other state economic indicators such as Texas employment growth, personal income, or sectoral employment growth. Because the Beige Book is released at least one month earlier than employment data and at least two years before GSP data, its timeliness makes it a good tool for current regional economic analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".