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Record W3095702697 · doi:10.3828/qs.2016.s5

Understanding Québec’s Strategic Choices in Domestic and Foreign Debt Markets

2016· article· en· W3095702697 on OpenAlexaboutno aff
Komla D. Dzigbede

Bibliographic record

VenueQuebec Studies · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
Fundersnot available
KeywordsDebtBondExternal debtWelfare economicsEconomicsLocal currencyMonetary economicsFinance

Abstract

fetched live from OpenAlex

Much of the discussion on Québec’s debt has centered on size and sustainability of provincial debt. As yet, there is no systematic study of the province’s strategic choices in debt markets, which choices result in the public debt figures that spawn concerns about debt sustainability among policymakers. This essay attempts to fill that research gap by analyzing the mix of factors that motivate Québec’s choice of debt habitat in domestic and foreign markets. Using a menu of estimation techniques, the essay finds that while a mix of bond-specific, institutional, and political factors explains whether Québec issues debt in domestic markets or foreign markets, risk factors in foreign markets tend to have a minimal influence on foreign currency habitat choice. Estimations are based on public accounts data (1960–2014) and information on 160 provincial bonds (1990–2014). Results provide an empirical framework for understanding the structure of debt issuance choices and should feed-forward to policy discussions on the character and sustainability of Québec’s debt burden.

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)0.002
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 score0.958
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.164
GPT teacher head0.280
Teacher spread0.115 · 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
Published2016
Admission routes1
Has abstractyes

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