Divisia monetary aggregates : theory and practice
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Bibliographic record
Abstract
List of Figures List of Tables List of Contributors Introductory Comments, Definitions, and Research on Indexes of Monetary Services M.T.Belongia PART I: NEW RESULTS IN THEORY AND PRACTICE Beyond the Risk-Neutral Utility Function W.A.Barnett & Y.Liu Neutral Networks with Divisia Money: Better Forecasts of Future Inflation? R.E.Dorsey PART II: EVIDENCE FROM EUROPEAN ECONOMIES AND THE PLANNED EMU AREA Weighted Monetary Aggregates for the U.K. L.Drake, K.A.Chrystal & J.M.Binner Weighted Monetary Aggregates for Germany H.Hermann, H.Reimers & K.Toedter Simple Sum v. Divisia Money in Switzerland: Some Empirical Results R.Fluri & E.Spoerndli The Relevance of Weighted Monetary Aggregates in the Netherlands N.G.J.Janssen & C.J.M.Kool Divisia Aggregates and the Demand for Money in Core EMU M.M.G.Fase PART III: EVIDENCE FROM THE PACIFIC BASIN Broad and Narrow Divisia Monetary Aggregates for Japan K.Ishida & K.Nakamura The Signals from Divisia Money in a Rapidly-Growing Economy J.H.Hahm & J.T.Kim Divisia Monetary Aggregates for Taiwan Y.C.Shih Weighted Monetary Aggregates: Empirical Evidence for Australia G.C.Lim & V.L.Martin PART IV: EVIDENCE FROM NORTH AMERICA The Canadian Experience with Weighted Monetary Aggregates D.Longworth & J.Atta-Mensah Consequences of Money Stock Mismeasurement: Evidence from Three Countries M.T.Belongia
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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) | 0.012 | 0.008 |
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