Bibliographic record
Abstract
In this paper we contrast a number of univariate models of Canadian GDP. Our preferred models are used to provide a business cycle chronology for Canada, which is compared with some existing, more judgmentally determined chronologies. We find that a simple, ‘two quarters of negative growth’ rule for determining recession dates is the most similar to our chronology. We also find that the most recent recession in Canada was unique in both its length and the slow speed of recovery. JEL Classification: C22, C51, C52, E32 Phases du cycle d'affaires au Canada. Dans ce mémoire, les auteurs contrastent un certain nombre de modèles du PIB canadien. Les modèles préférés sont utilisés pour définir une chronologie des cycles économiques du Canada qu'on peut comparer avec d'autres chronologies existantes basées davantage sur le jugement. On découvre que la règle “deux trimestres de croissance négative” est celle qui se rapproche le plus de la chronologie proposée quand il s'agit de définir les dates de récession. On découvre aussi que la récente récession canadienne a été unique tant par sa durée que par la lenteur avec laquelle la reprise subséquente s'est amorcée.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".