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Record W3125736396 · doi:10.1257/jel.20151354

<i>Current Federal Reserve Policy under the Lens of Economic History</i>: A Review Essay

2016· review· en· W3125736396 on OpenAlexaboutno aff
Stephen D. Williamson

Bibliographic record

VenueJournal of Economic Literature · 2016
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary reformMonetary policyEconomicsCentennialQuantitative easingOpen market operationIndependence (probability theory)Reserve requirementCentral bankFinancial systemMonetary economicsEconomic policyEconomyKeynesian economicsHistory

Abstract

fetched live from OpenAlex

This review essay reviews the volume edited by Owen Humpage, Current Federal Reserve Policy under the Lens of Economic History: Essays to Commemorate the Federal Reserve System’s Centennial, and provides a broader perspective on central-banking issues. The papers in the Humpage volume address various aspects of central banking history, money, and private banking, with a focus on putting recent Fed policies in perspective. The topics covered include the role of the central bank as lender of last resort, the effects of open-market operations versus central-bank lending, central-bank independence, the political economy of monetary unions, financial crises, the effects of unconventional monetary policies, commodity monies, and the Canadian financial system as a natural experiment. (JEL E32, E52, E58, G01, G21, G28, N10)

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.009
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.005

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.

Opus teacher head0.070
GPT teacher head0.320
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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