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
The dataset comprises monthly time series for exchange rates among the United States, Japan, Canada, the United Kingdom, France, Germany, and Italy. Explanatory variables include the output; the 3-month interest rates, the CPI, economic policy uncertainty indices, financial risk indicators such as implied equity market volatility (VIX), and geopolitical risk indicator, the U.S. monetary policy uncertainty, the U.S. trade policy uncertainty, the U.S. monetary policy surprise, term spread, and dividend yields. Macroeconomic series are drawn from the Federal Reserve Bank of St. Louis (FRED), OECD Main Economic Indicators, IMF International Financial Statistics, and national statistical agencies. Economic policy uncertainty and geopolitical risk indices come from policyuncertainty.com and the Caldara–Iacoviello dataset. Quarterly GDP data are interpolated to monthly frequency using the Chow–Lin method to match the frequency of other series. Monthly GDP data are obtained by interpolation. The EPU data are smoothed by a local level model. The explanatory data are transformed by natural logarithms. The sample spans January 1999 to March 2025, subject to data availability.
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 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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