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Record W3154101589 · doi:10.24908/iqurcp.9924

A Study of the Effect of Central Bank Intervention on North American Debt Financial Markets

2018· article· en· W3154101589 on OpenAlexvenueaboutno aff
Evan Burns

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCentral bankMonetary policyQuantitative easingCollateralFinancial systemDebtEconomicsAsset (computer security)Financial marketCapital marketChinese financial systemPresentation (obstetrics)BusinessFinanceMonetary economicsChinaPolitical science

Abstract

fetched live from OpenAlex

This presentation, and corresponding research paper, serves to analyze the impact of Central Bank policy and operations on financial markets in the United States and Canada during the period of Summer 2008 to present day. Research is primarily focused on the effects of monetary stimulus and the setting of Central Bank policy rates and how these two tools impacted the market for fixed income financial instruments. The presentation analyzes the decisions made by borrowers of capital, primarily institutions and governments, given the unprecedented macroeconomic environment experienced during this period. To complement this research and provide a thorough analysis of the effects of Central Bank policy, the presentation analyzes other asset classes and provides insight into the impact of Canadian and U.S. Central Bank actions globally. In doing this, the presentation will provide a more complete picture of how Central Bank actions during this period impacted not only North American economies but also the effects experienced by investors across geopolitical and asset class divisions.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.596

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.060
GPT teacher head0.332
Teacher spread0.272 · 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 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
Published2018
Admission routes2
Has abstractyes

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