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Record W7098229926

D E P E

2010· article· en· W7098229926 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies Worldwide
Canadian institutionsnot available
Fundersnot available
KeywordsInflation (cosmology)Money supplyMonetary policyInterest rateMoney creationOrder (exchange)Open market operationDemand depositCentral bankDemand for money
DOInot available

Abstract

fetched live from OpenAlex

In response to the recent financial crisis, central banks around the world, including the Bank of Canada, have provided markets with extraordinary levels of liquidity. As the economic recovery takes hold, the question arises of what the increased liquidity, through higher money growth, portends for the near future. The supply of and demand for money – a balance summarized in a “money gap ” estimate, which measures deviations in the actual supply of money relative to the demand for money driven by economic fundamentals – carries information on the outlook for inflation and economic growth. Current evidence suggests a rebound in economic growth and inflationary pressure unless the Bank begins to rein in money growth. The Bank of Canada should provide some guidance on its thinking on the behaviour of monetary and credit aggregates and what it entails for inflation and economic growth. Thor Koeppl, David Laidler, Michael Parkin, Angela Redish, Bill Robson, Avery Shenfeld, Andrew Spence, and others, provided helpful comments on earlier drafts. 1 As the Bank explained, “Conditional on the outlook for inflation, the target overnight rate can be expected to remain at its current level until the end of the second quarter of 2010 in order to achieve the inflation target. The Bank will continue to provide guidance in its scheduled interest rate announcements as long as the overnight rate is at the effective lower bound”

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0420.005

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 head0.005
GPT teacher head0.165
Teacher spread0.160 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2010
Admission routes1
Has abstractyes

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