U.S. Monetary Policy since the 1950s and the Changing Content of FOMC Minutes
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
Content analysis is used to analyze 60 years of Federal Open Market Committee (FOMC) minutes. Since there is no unique algorithm to quantify content, two different algorithms are applied. Wordscores compares content relative to a chosen benchmark, while DICTION is an alternative algorithm that is specifically designed to capture various elements that capture the sentiment or tone conveyed in a text. The resulting indicators are then incorporated into a VAR. The content of FOMC minutes is found to be significantly related to the state of the economy, notably real GDP growth, and changes in the fed funds rate. However, the relationship between content and macroeconomic conditions changes after 1993 when minutes are made public with a lag. Both content indicators also suggest substantive changes in the content of FOMC minutes since the 1950s in terms of the FOMC's dovishness or hawkishness.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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