Unconventional monetary policies, with a focus on quantitative easing
Bibliographic record
Abstract
This article distinguishes between credit easing policies and quantitative easing (QE) policies. The authors argue that there are two broad transmission mechanisms associated with quantitative easing: the Friedmanian mechanism, which is based on the theory of the money multiplier and the fractional-reserve banking system; and the Keynesian mechanism, advocated by Keynes in 1930, which relies on its impact on interest rates. The article also deals with the likely consequences of various incarnations of QE policies: QE done with banks, QE done with non-banks, QE for the people, Corbyn's people's QE and green QE. This is done by looking at the impact of these policies on the balance sheets of banks, private agents, the central bank and the government, and on their consequences for the fiscal balance of the government when taking into account the profits that are distributed by the central bank to the government. It is concluded that accounting tricks cannot modify reality.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".