Divisia monetary aggregates : theory and practice
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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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".