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Record W2942263513

Identification des points de retournement du cycle économique au Canada

2019· article· fr· W2942263513 on OpenAlexaboutno aff
Rachidi Kotchoni, Dalibor Stevanović, Stéphane Surprenant

Bibliographic record

VenueCIRANO Project Reports · 2019
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicRegional Economic and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Ce rapport propose d’établir un historique des cycles économiques au Canada et de comparer les cycles estimés à travers les di˙érentes régions et di˙érents secteur d’activité économique. Dans un premier temps, les cycles sont estimés sur des données d’emplois provinciales et sectorielles par des modèles à changements de régime marko-viens (Markov-switching models). Ils sont comparés à l’aide de mesures basées sur les corrélations des séries de probabilités de récession. Une certaine dispersion des cycles économiques semble exister au Canada, surtout entre l’est et l’ouest du pays, mais les cycles apparaissent relativement synchronisés. L’exercice est e˙ectué selon deux désai-sonnalisations des données d’emplois et il apparaît qu’un lissage plus important lors de la désaisonnalisation a˙ecte la saillance des points de retournement. Dans un deuxième temps, l’approche multivariée est proposée en tablant sur une base de données de plus de 150 variables macroéconomiques canadiennes et l’analyse en composantes princi-pales. Les résultats suggèrent encore des di˙érences cycliques entre l’est et l’ouest du pays et des cycles relativement synchronisés. Par contre, l’approche multivarié identi-fie les cycles plus uniformément à travers le pays que l’approche univariée basée sur l’emploi.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.202
Teacher spread0.183 · 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.

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
Published2019
Admission routes1
Has abstractyes

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