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Record W7149658434 · doi:10.17132/2693-3179.1360

Canada: Provincial Bond Purchase Program

2022· article· en· W7149658434 on OpenAlexaboutno aff
N Leonard

Bibliographic record

VenueJournal of Financial Crises · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsBondPortfolioGovernment (linguistics)NoticeBond marketFellFinancial marketSettlement (finance)Secondary market

Abstract

fetched live from OpenAlex

In the beginning of 2020, the outbreak of the novel coronavirus placed significant strain on financial markets and especially affected commodity-producing countries like Canada. As the broad economy contracted, oil-exports fell, and the government imposed public health restrictions to contain coronavirus, the Bank of Canada (BoC) announced emergency measures to ensure functioning of financial markets and to "reach companies and households and foster a robust recovery" (Poloz 2020, 1). One market that faced acute strain was the Canadian provincial bond market. The BoC announced the Provincial Bond Purchase Program (PBPP) through a notice published on April 15, 2020. The stated aim of the PBPP was "to maintain well-functioning provincial funding markets in the face of significant demands for funding as governments implement their emergency measures, and businesses and households seek to bridge this difficult period" (BoC 2020g). The PBPP was authorized to purchase up to CAD 50 billion of provincial bonds in the secondary market through primary dealers. The program was funded through settlement balances and the Government of Canada indemnified the program, along with other COVID-19 facilities. The PBPP's portfolio of provincial bonds reached a peak market value of CAD 17.6 billion on May 26, 2021. The PBPP had a minor impact on the overall size of the BoC balance sheet, but the announcement of the creation of the PBPP on April 15 had an immediate impact on provincial bond yields, which fell on average 21 basis points that day.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.289
Teacher spread0.270 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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