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Record W1652089129

The Roads Not Taken: Why the Bank of Canada Stayed With Inflation Targeting

2011· preprint· en· W1652089129 on OpenAlexaboutno aff
Christopher Ragan

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typepreprint
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsStatus quoInflation targetingMandateMonetary policyInflation (cosmology)EconomicsStatus quo biasMonetary economicsInternational economicsPolitical scienceMarket economyLaw
DOInot available

Abstract

fetched live from OpenAlex

Sticking with the status quo was only one option under debate among monetary experts in the lead-up to renewal of the Bank of Canada’s inflation-targeting mandate, which was announced this week. Several other routes were available. Two of them – namely, targeting nominal GDP or targeting full employment – were arguably non-starters. Two other approaches, however, held more promise: (i) moving to a price-level targeting regime, or (ii) sticking with inflation targeting but with a lower, say 1 percent, target. Nevertheless, the renewal of the status quo keeps in place a coherent monetary policy regime that has served Canadians well.

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)0.006
metaresearch head score (Gemma)0.026
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0230.014
Scholarly communication0.0130.004
Open science0.0020.003
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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
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

Citations4
Published2011
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicCanadian Policy and GovernanceFrench-language works237,207